{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c979f719-a7bd-4392-86f9-f699220b2c93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STATEFP20</th>\n",
       "      <th>COUNTYFP20</th>\n",
       "      <th>TRACTCE20</th>\n",
       "      <th>GEOID20</th>\n",
       "      <th>NAME20</th>\n",
       "      <th>NAMELSAD20</th>\n",
       "      <th>MTFCC20</th>\n",
       "      <th>FUNCSTAT20</th>\n",
       "      <th>ALAND20</th>\n",
       "      <th>AWATER20</th>\n",
       "      <th>...</th>\n",
       "      <th>P0050002</th>\n",
       "      <th>P0050003</th>\n",
       "      <th>P0050004</th>\n",
       "      <th>P0050005</th>\n",
       "      <th>P0050006</th>\n",
       "      <th>P0050007</th>\n",
       "      <th>P0050008</th>\n",
       "      <th>P0050009</th>\n",
       "      <th>P0050010</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>34</td>\n",
       "      <td>033</td>\n",
       "      <td>022100</td>\n",
       "      <td>34033022100</td>\n",
       "      <td>221</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>2588340</td>\n",
       "      <td>1040205</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-75.49497 39.57983, -75.49456 39.581...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>34</td>\n",
       "      <td>033</td>\n",
       "      <td>022202</td>\n",
       "      <td>34033022202</td>\n",
       "      <td>222.02</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>30901777</td>\n",
       "      <td>3656526</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-75.53401 39.54070, -75.53341 39.542...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>34</td>\n",
       "      <td>005</td>\n",
       "      <td>702300</td>\n",
       "      <td>34005702300</td>\n",
       "      <td>7023</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>1527134</td>\n",
       "      <td>44740</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>POLYGON ((-74.69783 39.96686, -74.69739 39.967...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>34</td>\n",
       "      <td>035</td>\n",
       "      <td>050801</td>\n",
       "      <td>34035050801</td>\n",
       "      <td>508.01</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>8663687</td>\n",
       "      <td>54171</td>\n",
       "      <td>...</td>\n",
       "      <td>122</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>122</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>POLYGON ((-74.67604 40.59714, -74.67600 40.597...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>34</td>\n",
       "      <td>035</td>\n",
       "      <td>052400</td>\n",
       "      <td>34035052400</td>\n",
       "      <td>524</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>15651373</td>\n",
       "      <td>126195</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>POLYGON ((-74.57130 40.70004, -74.57127 40.700...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 345 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  STATEFP20 COUNTYFP20 TRACTCE20      GEOID20  NAME20    NAMELSAD20 MTFCC20  \\\n",
       "0        34        033    022100  34033022100     221  Census Tract   G5020   \n",
       "1        34        033    022202  34033022202  222.02  Census Tract   G5020   \n",
       "2        34        005    702300  34005702300    7023  Census Tract   G5020   \n",
       "3        34        035    050801  34035050801  508.01  Census Tract   G5020   \n",
       "4        34        035    052400  34035052400     524  Census Tract   G5020   \n",
       "\n",
       "  FUNCSTAT20   ALAND20  AWATER20  ... P0050002 P0050003 P0050004 P0050005  \\\n",
       "0          S   2588340   1040205  ...        0        0        0        0   \n",
       "1          S  30901777   3656526  ...        0        0        0        0   \n",
       "2          S   1527134     44740  ...        0        0        0        0   \n",
       "3          S   8663687     54171  ...      122        0        0      122   \n",
       "4          S  15651373    126195  ...        0        0        0        0   \n",
       "\n",
       "  P0050006 P0050007 P0050008 P0050009 P0050010  \\\n",
       "0        0        0        0        0        0   \n",
       "1        0        0        0        0        0   \n",
       "2        0        5        0        0        5   \n",
       "3        0        5        0        0        5   \n",
       "4        0       18        0        0       18   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((-75.49497 39.57983, -75.49456 39.581...  \n",
       "1  POLYGON ((-75.53401 39.54070, -75.53341 39.542...  \n",
       "2  POLYGON ((-74.69783 39.96686, -74.69739 39.967...  \n",
       "3  POLYGON ((-74.67604 40.59714, -74.67600 40.597...  \n",
       "4  POLYGON ((-74.57130 40.70004, -74.57127 40.700...  \n",
       "\n",
       "[5 rows x 345 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this is for working with census tracts and VTD precincts\n",
    "from shapely.geometry import Point, LineString, Polygon\n",
    "import shapely\n",
    "import geopandas as gpd\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from numpy import random\n",
    "from scipy.stats import norm\n",
    "import math\n",
    "tractPopFile = gpd.read_file(\"state_map_files/nj_pl2020_t.dbf\") #for Texas, need only this file\n",
    "tractPopFile.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "208d6842-f420-4205-859d-888e06316d7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#handy function for plotting Polygon or multiPolygon tracts and precincts\n",
    "def plotPoly(inputPoly):\n",
    "    simplePoly = Polygon([(0,0),(0,1),(1,1)])\n",
    "    if inputPoly.type == simplePoly.type:\n",
    "        x,y = inputPoly.exterior.xy\n",
    "        plt.plot(x,y)\n",
    "    else:\n",
    "        for geom in inputPoly.geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a7a90b04-4b48-4ab8-9195-8d953907c3fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are 2181 popn tracts for NJ\n"
     ]
    }
   ],
   "source": [
    "# EXTRACT TRACT GEOMETRIES AND POPULATIONS INTO LISTS, COMPUTE TRACT AREAS\n",
    "# If the population and geometry data are in one file, this should work.\n",
    "STATE = \"NJ\"\n",
    "tractGeom = tractPopFile['geometry']  #for some states, replace with tractGeomFile\n",
    "tractPop = tractPopFile['P0010001']\n",
    "tractVAP = tractPopFile['P0030001']   #NEW 3/2/22 - USE VAP\n",
    "# tractPop2 = tractPopFile['P0020001']   #not needed; confirmed that this matches P00100001 exactly\n",
    "tractHisp = tractPopFile['P0040002']   #NEW 3/2/22 - USE VAP\n",
    "tractBlack = tractPopFile['P0030004']  #NEW 3/2/22 - USE VAP\n",
    "nTracts = len(tractPop)\n",
    "print(\"there are {0} popn tracts for {1}\".format(nTracts, STATE) )\n",
    "tractArea = [0.]*nTracts\n",
    "for t in range (0,nTracts) :\n",
    "    tractArea[t] = tractGeom[t].area\n",
    "isSkippedTract = [0] *nTracts  #this will house a temporary list of tracts for manipulation\n",
    "tractPop = tractPop.to_numpy()  #to avoid panda overwrite grousing\n",
    "tractBlack= tractBlack.to_numpy()\n",
    "tractHisp = tractHisp.to_numpy()\n",
    "tractVAP = tractVAP.to_numpy()\n",
    "stateVAP = np.sum(tractVAP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c90b3908-e2e3-46b8-98e5-ae285eca1ee3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is Hispanic population by total population NJ voter-age population\n",
      "state pop= 9288994 VAP pct Hispanic is  0.1972171216443195\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#What is our correlation of Hispanic pop to total pop?\n",
    "print(\"this is Hispanic population by total population \"+STATE,\"voter-age population\")\n",
    "print(\"state pop=\",np.sum(tractPop), \"VAP pct Hispanic is \",np.sum(tractHisp)/np.sum(tractVAP) )\n",
    "fig, ax = plt.subplots()\n",
    "ax.set(xlabel=\"tract Voter Age Population\", ylabel=\"tract Hispanic pop\")\n",
    "x = [0,10000]\n",
    "y = [0,10000]\n",
    "plt.plot(tractVAP, tractHisp, marker='.',linestyle=\"none\")\n",
    "plt.plot(x,y,linestyle = 'dashed')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "237df16d-af87-47ba-acb1-2ef5702c1406",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is voter-age Black population by total VAP NJ\n",
      "total state pop= 9288994 , VAP pct Black is  0.12856518950573453\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#What is our correlation of Black pop to total pop?\n",
    "print(\"this is voter-age Black population by total VAP \"+STATE)\n",
    "print(\"total state pop=\",np.sum(tractPop), \", VAP pct Black is \",np.sum(tractBlack)/np.sum(tractVAP) )\n",
    "fig, ax = plt.subplots()\n",
    "ax.set(xlabel=\"tract VAP\", ylabel=\"tract Black VAP\")\n",
    "x = [0,10000]\n",
    "y = [0,10000]\n",
    "plt.plot(tractVAP, tractBlack, marker='.',linestyle=\"none\")\n",
    "plt.plot(x,y,linestyle = 'dashed')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "10b208af-8eb0-45fa-9dc0-8873eb797200",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of Census tract population for NJ\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#for curiosity, let's plot the distribution of populations among tracts\n",
    "n_bins=50\n",
    "print(\"this is a histogram of Census tract population for \"+STATE)        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(tractPop, bins=n_bins)\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4aac60d6-4cce-469b-b075-ad743acda38c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is population by Census tract for NJ\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#What is our distribution of tract populations by area?\n",
    "print(\"this is population by Census tract for \"+STATE)  \n",
    "plt.plot(tractPop, tractArea, marker='.',linestyle=\"none\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f159bf55-a25e-45c0-b7ea-4c21fbce9bb0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'shapely.geometry.polygon.Polygon'> <class 'shapely.geometry.polygon.Polygon'>\n"
     ]
    }
   ],
   "source": [
    "print( type(tractGeom[0]),type(tractGeom[1]) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5e8f8f92-a336-43e5-8ac5-20c657cb5857",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "looking for nonPolygons in census tract data\n",
      "102 4124 0.0004602463274999604 nonPolygon tract no, pop, area\n",
      "0.00043966658949996513\n",
      "2.057973799999526e-05\n",
      "1869 1949 0.00027590647050001446 nonPolygon tract no, pop, area\n",
      "0.00011402896800001695\n",
      "0.0001618775024999975\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYkAAAD4CAYAAAAZ1BptAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8/fFQqAAAACXBIWXMAAAsTAAALEwEAmpwYAAAZQklEQVR4nO3de3RV9Z338ff3nAQSQiIEEi6mEK2oreClpIpluKigCAh09Sljp7J8+nSVOuOtYy3VcdlOp+qDt1W71jNOHwS78Fqro4NFagUsXkbRHqoiXqmICCIEwiWBECD5zh/Z4AHOj5wk53AO+nmtlbVvv9/e3/NbLD757X02mLsjIiKSSizXBYiISP5SSIiISJBCQkREghQSIiISpJAQEZGgglwXkErv3r29uro612WIiBw1li1btsndKzJ93rwMierqahKJRK7LEBE5apjZR9k4r243pemCCy6goqKCm266CQB358orr2TEiBFMnDiRuro6AH70ox8xbNgwhg0bxsyZM3NZsohIpykk0jRnzhxuv/32/dt/+tOf2LlzJy+88AJTp07ltttuA+Dyyy9n6dKlvPTSS8ybN48PPvggVyWLiHSaQiJNVVVVB2wvWbKEiRMnAnDRRRfx/PPPAzBo0CAAYrEY8XiceDx+ZAsVEckghUQH1dXV0bNnTwB69Oix/3bTPvfffz9f/vKX0QN4ETma5eWD66NBeXk5W7duBWDbtm37AwNg0aJFzJ07lz/84Q85qk5EJDM0k+igUaNGsWDBAgAWLFjAqFGjAHjllVe48cYbeeyxxyguLs5liSIinaaZRJp+8IMf8NJLL9HU1EQikeDxxx9n/vz5jBgxgrKyMu677z4Avv/97wMwZcoUAO68806GDh2aq7JFRDrF8vGfCq+pqfGOvifh7qzb2siuPc3s2tNC094Wdu9tocWd5hanxR13aHGnZd+yJWk9Oh5se1D7fceb960nHYvHjJrqnvQtK6J71wJKuhZQXBgnFrMMj5iIfNGZ2TJ3r8n0eT93M4mHX/2Yf3nizVyXEWQGfUqLOLXqGArjMaadPZBhx/fKdVkiIimlHRJmFgcSwDp3n2hm5cAjQDWwGpjq7ltS9Lsa+AFgwD3uflfnyw57YOlnLx3+/2lD6VoQo0tBjIJYjJiBmRGPGTGDmBkWLWNmxGOtx1u3Dzwejx3Ytq1zxQx27G7mLx/WsbVxNw1Nzexs2svG+ibmvPghn769C4Al723krX8bl80hERHpsPbMJK4G3gHKou3rgMXuPtPMrou2f5rcwcwG0xoQZwK7gafN7Cl3X9npylN4ZfETzKq7lr8VHst/MZp7/7gb69INupTQpUsXigtjFBfGKe4SpzAe48zjypl4av+U52ppcV77eCvNLU5B3CiItf7l36t7FypLi4inccuoe9cCzjm5EoA3127joVfX8OTr6wA4qU8pk8/oz7eHfilzAyAikmFphYSZVQETgJuBa6Ldk4HR0fpcYAkHhQTwFWCpu++MzvMc8E3gts4UndKeRs564X+DQVV8E6N5Axo+O/yzY27mFU5l195mdjQ1s6mhifte/ohjigs5qW8plaVFB5zuP/+6lp88tjzlpeIxo29Za/utO3fTuKeZkq4FlBUVUlZcSGlR63ppUQGNu5tZvXkH735aT1FhjImn9uc7Zw7gawN6YKZnEyKS39KdSdwFzABKk/b1cff1AO6+3swqU/RbAdxsZr2ARmA8rbesDmFm04HpAAMGDEizrCRtPID/t6lnw7Gt3zLataeZv7v1z2xqaGLanFc5priQv944ltr6Jj7espP3Pq3nzmfeA+Df/+FrlHSNs7e59eF0bX0Tn2xtZP22XcTMOKa4kOIuMXY0NbN91x62N+6lftce1m1tpKFpD0UFcSpKu/KLSacw5YxjOaa4sP2fTUQkR9oMCTObCGx092VmNro9J3f3d8zsVmAhrb/XvwHsDbSdBcyC1m83tec6AHTpBj+rg4aNsGEFfPombHgL3p4HLXuga9n+pkWFcZ6fMZop//7fvL+hgbLiAob938XU1jftb3NCZXcevewbnFDZvd2liIh8XqQzkxgOTDKz8UARUGZmDwAbzKxfNIvoB2xM1dnd5wBzAMzsFmBtZkpPIRaHsn6tP4PGRjvntM4yDrq1s3XnHjZGofBxXSMjBvXmqnNPoKq8GwPLu1Hdq0RfVRWRL7x2vScRzSSujb7ddDuwOenBdbm7z0jRp9LdN5rZAOAZ4OxU34JK1pn3JNqjucX5aPMOtu/ay2lVx+gZgYgctfLxPYmZwO/N7PvAGuDbAGbWH5jt7uOjdv8ZPZPYA1zeVkAcSfGYcXyFbieJiIS0KyTcfQmt32LC3TcD56Vo8wmtD6j3bY/oVIUiIpIz+gf+REQkSCEhIiJBCgkREQlSSIiISJBCQkREghQSIiISpJAQEZEghYSIiAQpJEREJEghISIiQQoJEREJUkiIiEiQQkJERIIUEiIiEqSQEBGRIIWEiIgEKSRERCRIISEiIkEKCRERCVJIiIhIkEJCRESCFBIiIhKkkBARkSCFhIiIBCkkREQkSCEhIiJBCgkREQlSSIiISJBCQkREgtIOCTOLm9lrZjY/2i43s4VmtjJa9gz0+2cze8vMVpjZw2ZWlKniRUQku9ozk7gaeCdp+zpgsbsPAhZH2wcws2OBq4Aadx8MxIGLO16uiIgcSWmFhJlVAROA2Um7JwNzo/W5wJRA9wKg2MwKgG7AJx2qVEREjrh0ZxJ3ATOAlqR9fdx9PUC0rDy4k7uvA+4A1gDrgW3u/kyqC5jZdDNLmFmitrY2/U8gIiJZ02ZImNlEYKO7L2vvyaPnFJOB44D+QImZXZKqrbvPcvcad6+pqKho76VERCQL0plJDAcmmdlq4HfAuWb2ALDBzPoBRMuNKfqOAT5091p33wM8DnwjI5WLiEjWtRkS7n69u1e5ezWtD52fdfdLgCeBS6NmlwLzUnRfAwwzs25mZsB5HPjwW0RE8lhn3pOYCYw1s5XA2GgbM+tvZgsA3P0V4DHgr8Cb0fVmdapiERE5Yszdc13DIWpqajyRSOS6DBGRo4aZLXP3mkyfV29ci4hIkEJCRESCFBIiIhKkkBARkSCFhIiIBCkkREQkSCEhIiJBCgkREQlSSIiISJBCQkREghQSIiISpJAQEZEghYSIiAQpJEREJEghISIiQQoJEREJUkiIiEiQQkJERIIUEiIiEqSQEBGRIIWEiIgEKSRERCRIISEiIkEKCRERCVJIiIhIkEJCRESCFBIiIhKkkBARkaC0Q8LM4mb2mpnNj7bLzWyhma2Mlj1T9DnJzF5P+tluZj/KYP0iIpJF7ZlJXA28k7R9HbDY3QcBi6PtA7j7e+5+urufDgwFdgJPdLxcERE5ktIKCTOrAiYAs5N2TwbmRutzgSltnOY84AN3/6idNYqISI6kO5O4C5gBtCTt6+Pu6wGiZWUb57gYeDh00Mymm1nCzBK1tbVpliUiItnUZkiY2URgo7sv6+hFzKwLMAl4NNTG3We5e42711RUVHT0UiIikkEFabQZDkwys/FAEVBmZg8AG8ysn7uvN7N+wMbDnONC4K/uvqHzJYuIyJHS5kzC3a939yp3r6b1ltGz7n4J8CRwadTsUmDeYU7zHQ5zq0lERPJTZ96TmAmMNbOVwNhoGzPrb2YL9jUys27R8cc7U6iIiBx56dxu2s/dlwBLovXNtH5j6eA2nwDjk7Z3Ar06U6SIiOSG3rgWEZEghYSIiAQpJEREJEghISIiQQoJEREJUkiIiEiQQkJERIIUEiIiEqSQEBGRIIWEiIgEKSRERCRIISEiIkEKCRERCVJIiIhIkEJCRESCFBIiIhKkkBARkSCFhIiIBCkkREQkSCEhIiJBCgkREQlSSIiISJBCQkREghQSIiISpJAQEZEghYSIiAQpJEREJEghISIiQQoJEREJSjskzCxuZq+Z2fxou9zMFprZymjZM9Cvh5k9Zmbvmtk7ZnZ2pooXEZHsas9M4mrgnaTt64DF7j4IWBxtp/Jr4Gl3Pxk47aBziIhIHksrJMysCpgAzE7aPRmYG63PBaak6FcGjATmALj7bnff2vFyRUTkSEp3JnEXMANoSdrXx93XA0TLyhT9jgdqgd9Gt6pmm1lJqguY2XQzS5hZora2Nu0PICIi2dNmSJjZRGCjuy/rwPkLgK8B/+HuZwA7CNyWcvdZ7l7j7jUVFRUduJSIiGRaOjOJ4cAkM1sN/A4418weADaYWT+AaLkxRd+1wFp3fyXafozW0BARkaNAmyHh7te7e5W7VwMXA8+6+yXAk8ClUbNLgXkp+n4KfGxmJ0W7zgPezkThIiKSfZ15T2ImMNbMVgJjo23MrL+ZLUhqdyXwoJktB04HbunENUVE5Agyd891DYeoqanxRCKR6zJERI4aZrbM3WsyfV69cS0iIkEKCRERCVJIiIhIkEJCRESCFBIiIhKkkBARkSCFhIiIBCkkREQkSCEhIiJBCgkREQlSSIiISJBCQkREghQSIiISpJAQEZEghYSIiAQpJEREJEghISIiQQoJEREJUkiIiEiQQkJERIIUEiIiEqSQEBGRIIWEiIgEKSRERCRIISEiIkEKCRERCVJIiIhIkEJCRESC0g4JM4ub2WtmNj/aLjezhWa2Mlr2DPRbbWZvmtnrZpbIVOEiIpJ97ZlJXA28k7R9HbDY3QcBi6PtkHPc/XR3r+lAjSIikiNphYSZVQETgNlJuycDc6P1ucCUjFYmIiI5l+5M4i5gBtCStK+Pu68HiJaVgb4OPGNmy8xseugCZjbdzBJmlqitrU2zLBERyaY2Q8LMJgIb3X1ZB68x3N2/BlwIXG5mI1M1cvdZ7l7j7jUVFRUdvJSIiGRSOjOJ4cAkM1sN/A4418weADaYWT+AaLkxVWd3/yRabgSeAM7MQN0iInIEtBkS7n69u1e5ezVwMfCsu18CPAlcGjW7FJh3cF8zKzGz0n3rwPnAigzVLiIiWdaZ9yRmAmPNbCUwNtrGzPqb2YKoTR/gRTN7A3gVeMrdn+5MwSIicuQUtKexuy8BlkTrm4HzUrT5BBgfra8CTutskSIikht641pERIIUEiIiEqSQEBGRIIWEiIgEKSRERCRIISEiIkEKCRERCVJIiIhIkEJCRESCFBIiIhKkkBARkSCFhIiIBCkkREQkSCEhIiJBCgkREQlSSIiISJBCQkREghQSIiISpJAQEZEghYSIiAQpJEREJEghISIiQQoJEREJUkiIiHTABRdcQEVFBTfddBMAW7Zs4fzzz2fUqFEMHz6c5cuXA9Dc3My1117LmDFjGD16NG+//TYATz75JGeddRYjRozgwQcfzNnnaEtBrgsQETkazZkzh0WLFrF27VoAHnzwQYYPH87Pf/5zlixZws0338wjjzzCrFmzOPHEE7njjjv2921paeEnP/kJiUSCoqIiRo4cyYQJE+jRo0eOPk2YZhIiIh1QVVV1wPZXvvIVtm/fDkBdXR2VlZUAPProo3z00Uecc845XHHFFezevZtNmzZRUVFBaWkphYWFnHjiibz66qtH/DOkQyEhIpIBQ4cOZenSpQwePJirrrqKH//4xwCsW7eOfv368ec//5mioiLuvfdeKioq2LRpE+vWrWP79u28+OKL1NXV5fgTpKaQEBHJgNtuu41vfetbrFixgkcffZTLL78cgPLycsaNGwfAuHHjWL58OWbGrFmzmDZtGt/97ncZMmQI/fv3z2X5QXomISKSpLlhB7W//jUtO3fQ5UsDKOjbh67HH0/Xk08m1qVLsJ+707t3bwAqKyv3zwxGjx5NIpHghBNO2L8EGDlyJM8++yz19fV885vf5Kyzzsr+h+sAc/f0GprFgQSwzt0nmlk58AhQDawGprr7lnT6tnWtmpoaTyQSadUlItJZLU1NNDz3HNufWkDDkiV4U9OhjQoLiZeVUTT4FPrPnMk/zpjBSy+9RFNTE4MHD+buu+9m2rRpNDc309jYyK233sro0aPZsmUL3/ve99i6dSvl5eXcf//9lJSUMGPGDP7yl79QUFDALbfcwte//vVOfQYzW+buNZ06SarztiMkrgFqgLIoJG4D6tx9ppldB/R095+m07etaykkRCTbfO9edix9he1PPUX9woW0NDQQ79WLsnHjKJswgeIzTscbG9mzYQNbHniQLUlfUx348EN0O+OMHFZ/qGyFRFq3m8ysCpgA3AxcE+2eDIyO1ucCS4BDQiLQV0TkiPOWFhpfe43tTy1g+9NP01xXR6x7d0rHjqVswgRKhp2FFXz21+LudetYNWkyuBM/5hh6TJ1Kz4v/HisuPux13t/yPi+ue5HGvY2MqhrFV3t9lZgdnY+A030mcRcwAyhN2tfH3dcDuPt6M6tsR99DmNl0YDrAgAED0ixLROTwvLmZurn30fDC8+x8eSkAVlhI93PPpWziBLqPHEmsa9eUffduroN9d1sKCth8zz1svuceAL40ezbd/244APNXzeeRdx+huKCYl9e/fMA5fvPGb+hV1IsxA8fww1N/SEW3iix90uxo83aTmU0Exrv7P5nZaODa6HbTVnfvkdRui7v3TKdvW0XpdpOIZMqu99/nw0mTD9kfKykhVlpKvLQ7sdIyYqXdiXcvbV2Wlrbu615CYyLB9gV/PKBvcc1QBt57LxY9yB4yd0jKa1922mUMKB3AC2tfYNGaRRQVFPGLb/yCsQPHZvxz5vJ203BgkpmNB4qAMjN7ANhgZv2iWUQ/YGO6fd39kkx9ABGRw+laXU3PadMoOvkkwGhpqKe5voGW+u3Rsp7m+nqaN21m9+rVtNQ30FxfD3v2tJ4gHqfb2cMoHTOG0jFjKOzT55BrXFh9IX9c3RokC//XQvp064OZ7T9+0ZcvYvW21Vz3wnVcs+QaJhw/gWuGXkNlt9ANmPyR9oNrgINmErcDm5MeXJe7+4x0+rZ1Hc0kRCSX3B1vaqKlvh4rLibevfth2+/Ys4NhDw3bv923pC9Deg9hQOkA+pX0o29JX/qW9KV3cW8eevchfrvitxTECvjhqT9k2len0SUe/mptunL+7aaoiNF8FhK9gN8DA4A1wLfdvc7M+gOz3X18qG9b11FIiMjRpqm5iXfr3uXN2jdZXrucFZtXsL5hPXt97wHtSruU0iXWhc27Nu/fd+FxFzJ9yHRO6HlCh6+fFyFxpCgkROTzoLmlmc27NrN+x3o+3fEpn+74lDXb17Bq2ypWbVtF3a4D/ymOM/ueyZwL5nToWjn9CqyIiLRfPBanslslld0qOa3itEOO1+2qY9XWVSQ2JLj79bsZM3BMDqo8PM0kREQ+B7I1kzg63+4QEZEjQiEhIiJBCgkREQlSSIiISJBCQkREghQSIiISpJAQEZEghYSIiATl5ct0ZlYLfJSjy/cGNuXo2u2lWjPvaKkTVGu2HK21DnT3jP9nFXkZErlkZolsvLWYDao1846WOkG1ZotqPZBuN4mISJBCQkREghQSh5qV6wLaQbVm3tFSJ6jWbFGtSfRMQkREgjSTEBGRIIWEiIgEfSFCwsweMbPXo5/VZvb6QccHmFmDmV0b6P+vZrYu6Rzjo/3VZtaYtP83+VprdOx6M/ubmb1nZhfkutakdteamZtZ72g778Y1VGu0L6/G1cx+aWbLo/7PRP/vfF6Oa6jW6Fi+jevtZvZuVO8TZtYj2p/Rcc1WndGxdo/pF+K/L3X3v9+3bmZ3AtsOavIr4I9tnOZX7n5Hiv0fuPvpnavwM9mq1cy+ClwMnAL0BxaZ2Ynu3pzLWs3sS8BYYM1Bh/JuXFPVmqfjeru73xj1vwr4GXBZdCzfxjVlrXk6rguB6919r5ndClwP/DQ6lrFxzVadHR3TL8RMYh8zM2Aq8HDSvinAKuCtHJWVUhZqnQz8zt2b3P1D4G/AmRkotbO1/gqYARyRb1Bkoda8G1d33560WcIRGNss1JqP4/qMu++NNpcCVZmoJyQLdXZoTL9QIQGMADa4+0oAMyuh9TeBX6TR94po+navmfVM2n+cmb1mZs+Z2Yg8rvVY4OOkNmujfTmr1cwmAevc/Y0Uh/NqXA9Ta96Na9T2ZjP7GPgurb+d75NX43qYWvNyXJP8Hw78bT4b45rpOjs0pp+b201mtgjom+LQDe4+L1r/DkmpTOtg/8rdG1pDO+g/gF/S+lvOL4E7aR389cAAd99sZkOB/zKzUw767Shfak3Vqc3fMLNVq5l1A24Azk9xOK/GtY1a82pc9xfgfgNwg5ldD1wB/Jw8G9c2as3LcY2ucQOwF3gw2tXucc1RnR0aU9z9C/FDayBuAKqS9r0ArI5+tgJ1wBVtnKcaWBE4tgSoycdaab0veX3SsT8BZ+eqVmAIsDGp3V5a7/X3zbdxPVyt+TauKc4z8Cj687q/1nwdV+BS4GWg22Gu0elxzUadHR3TTg340fQDjAOeO8zxfwWuDRzrl7T+z7Te1wOoAOLR+vHAOqA8T2s9BXgD6AocR+t9zXguaz2o3Wqgd76O62FqzbtxBQYlrV8JPJav43qYWvNxXMcBbwMVB+3P+Lhmqc4OjekX6ZnExRw4dTssM5ttZvv+dcXbzOxNM1sOnEPrX74AI4HlZvYG8BhwmbvX5WOt7v4W8Hta//A8DVzunfimSIZqDcnHcU0pT8d1ppmtiP4MnA9cHe3Px3FNWWuejuv/A0qBhXbgV12zMa4Zr7OjY6p/lkNERIK+SDMJERFpJ4WEiIgEKSRERCRIISEiIkEKCRERCVJIiIhIkEJCRESC/ged2W6uHUf50gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"looking for nonPolygons in census tract data\")\n",
    "tractFixList = [-99999]\n",
    "notPoly = [0]*nTracts\n",
    "for m in range(nTracts):\n",
    "    if type(tractGeom[m]) != type(tractGeom[1]):  #assuming tract 1 is a single polygon\n",
    "        notPoly[m] = 1\n",
    "        print(m,tractPop[m], tractArea[m], \"nonPolygon tract no, pop, area\")\n",
    "        if tractFixList == [-99999] :\n",
    "            tractFixList = [m]\n",
    "        else:\n",
    "            tractFixList.append[m]\n",
    "        x = tractGeom[m].centroid.x\n",
    "        y = tractGeom[m].centroid.y\n",
    "        if y < 999 :  #let's zoom in on these\n",
    "            for geom in tractGeom[m].geoms :\n",
    "                xg,yg = geom.exterior.xy\n",
    "                print(geom.area)\n",
    "                plt.plot(xg,yg)\n",
    "            plt.text(x+0.0,y+0.01,m,fontsize=9)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d34b5fe5-6f02-413a-8c7b-c6662fa756ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#convex-hull these right away  (decision for each state - yes for NJ)\n",
    "# tractFixList = [102,1869]   #uncomment if need to manually override list from above block\n",
    "for m in tractFixList:\n",
    "    plotPoly(tractGeom[m])\n",
    "    tractGeom[m] = tractGeom[m].convex_hull\n",
    "    plotPoly(tractGeom[m])\n",
    "    notPoly[m] = 0\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a05c0ec2-d208-45b8-8ff6-c1f1f583ad5d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[208, 349, 376, 1583, 1685, 1995, 1996, 1997]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# For potential later issues, identify and visualize low-population tracts.  #SEE FLORIDA CODE FOR PENINSULA COMPRESSION EXAMPLES\n",
    "oceanTracts = [0]\n",
    "for m in range(nTracts):\n",
    "    x = tractGeom[m].centroid.x\n",
    "    y = tractGeom[m].centroid.y\n",
    "    \n",
    "    if(notPoly[m] == 0) :\n",
    "        indent = \"indent\"\n",
    "        #x2,y2 = tractGeom[m].exterior.xy  #turn these two on to show state map\n",
    "        #plt.plot(x2,y2,c=\"purple\")\n",
    "    if(tractPop[m] < 100 ):  #and x > -98 to show Gulf Coast only\n",
    "        #print( m,tractPop[m],\"(\",x,\",\",y,\")\" , tractGeom[m].area)\n",
    "        x2,y2 = tractGeom[m].exterior.xy  #turn these two on to show state map\n",
    "        plt.plot(x2,y2,c=\"purple\")\n",
    "        plt.text(x+0.03, y+0.03,m, fontsize=9)\n",
    "        #plt.scatter(x,y)\n",
    "        if y < 40.4 : #approx cutoff for SE ocean tracts\n",
    "            if oceanTracts == [0]:\n",
    "                oceanTracts = [m]\n",
    "            else:\n",
    "                oceanTracts.append(m)\n",
    "    else:\n",
    "        indent = \"indent\"\n",
    "        # plt.scatter(x,y)\n",
    "print(oceanTracts)  # see a later block for assigning these as skipped tracts\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "da1f99a3-04d0-427b-95e3-d9a57836959d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# OK, these numbered tracts should be skipped. \n",
    "for t in oceanTracts:\n",
    "    plotPoly(tractGeom[t])\n",
    "    isSkippedTract[t] = 1\n",
    "                \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b0e9e246-a558-475c-8181-075a9421034e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2181 8\n"
     ]
    }
   ],
   "source": [
    "print(len(isSkippedTract),np.sum(isSkippedTract))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d63968a2-afc1-4913-863c-4c415fb9c0b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "looking for tracts with zero area\n"
     ]
    }
   ],
   "source": [
    "# For potential later issues, identify and visualize zero-Area tracts.  See Ohio code for more code\n",
    "#isSkippedTract = [0]*nTracts\n",
    "print(\"looking for tracts with zero area\")\n",
    "for m in range(nTracts):\n",
    "    if(tractArea[m] == 0):       \n",
    "        x = tractGeom[m].centroid.x\n",
    "        y = tractGeom[m].centroid.y\n",
    "        print( m,tractPop[m],\"(\",x,\",\",y,\")\" )\n",
    "        x2,y2 = tractGeom[m].exterior.xy\n",
    "        plt.plot(x2,y2,c=\"purple\")\n",
    "        #print(tractGeom[m])\n",
    "        plt.scatter(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "72abde7f-085d-4c0a-abe3-29e8d6203b40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CONGR_DIST</th>\n",
       "      <th>COUNTY</th>\n",
       "      <th>MUN_NAME</th>\n",
       "      <th>G20PREDBID</th>\n",
       "      <th>G20PRERTRU</th>\n",
       "      <th>G20PRELJOR</th>\n",
       "      <th>G20PREGHAW</th>\n",
       "      <th>G20PRECBLA</th>\n",
       "      <th>G20PRESLAR</th>\n",
       "      <th>G20PREOHAM</th>\n",
       "      <th>G20PREOFUE</th>\n",
       "      <th>G20USSDBOO</th>\n",
       "      <th>G20USSRMEH</th>\n",
       "      <th>G20USSGHOF</th>\n",
       "      <th>G20USSOFER</th>\n",
       "      <th>G20USSOBUR</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>02</td>\n",
       "      <td>Atlantic</td>\n",
       "      <td>Buena Borough</td>\n",
       "      <td>964</td>\n",
       "      <td>1140</td>\n",
       "      <td>15</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>950</td>\n",
       "      <td>1057</td>\n",
       "      <td>14</td>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "      <td>POLYGON ((372203.098 248033.314, 372179.200 24...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>02</td>\n",
       "      <td>Atlantic</td>\n",
       "      <td>Margate City</td>\n",
       "      <td>1865</td>\n",
       "      <td>1945</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1834</td>\n",
       "      <td>1907</td>\n",
       "      <td>7</td>\n",
       "      <td>14</td>\n",
       "      <td>7</td>\n",
       "      <td>POLYGON ((489588.574 177413.205, 489558.719 17...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>02</td>\n",
       "      <td>Atlantic</td>\n",
       "      <td>Mullica Township</td>\n",
       "      <td>1306</td>\n",
       "      <td>2085</td>\n",
       "      <td>25</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1262</td>\n",
       "      <td>2014</td>\n",
       "      <td>17</td>\n",
       "      <td>37</td>\n",
       "      <td>7</td>\n",
       "      <td>POLYGON ((427942.516 266334.807, 426253.899 26...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>02</td>\n",
       "      <td>Atlantic</td>\n",
       "      <td>Egg Harbor Township</td>\n",
       "      <td>12986</td>\n",
       "      <td>11995</td>\n",
       "      <td>188</td>\n",
       "      <td>71</td>\n",
       "      <td>25</td>\n",
       "      <td>40</td>\n",
       "      <td>23</td>\n",
       "      <td>14</td>\n",
       "      <td>12585</td>\n",
       "      <td>11533</td>\n",
       "      <td>97</td>\n",
       "      <td>295</td>\n",
       "      <td>73</td>\n",
       "      <td>MULTIPOLYGON (((461924.197 182829.378, 461894....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>02</td>\n",
       "      <td>Atlantic</td>\n",
       "      <td>Atlantic City</td>\n",
       "      <td>8148</td>\n",
       "      <td>2541</td>\n",
       "      <td>34</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>7936</td>\n",
       "      <td>2161</td>\n",
       "      <td>42</td>\n",
       "      <td>145</td>\n",
       "      <td>29</td>\n",
       "      <td>POLYGON ((503633.330 186284.284, 503592.672 18...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  CONGR_DIST    COUNTY             MUN_NAME  G20PREDBID  G20PRERTRU  \\\n",
       "0         02  Atlantic        Buena Borough         964        1140   \n",
       "1         02  Atlantic         Margate City        1865        1945   \n",
       "2         02  Atlantic     Mullica Township        1306        2085   \n",
       "3         02  Atlantic  Egg Harbor Township       12986       11995   \n",
       "4         02  Atlantic        Atlantic City        8148        2541   \n",
       "\n",
       "   G20PRELJOR  G20PREGHAW  G20PRECBLA  G20PRESLAR  G20PREOHAM  G20PREOFUE  \\\n",
       "0          15           8           0           4           3           3   \n",
       "1          11           8           2           2           2           2   \n",
       "2          25           8           2           3           2           1   \n",
       "3         188          71          25          40          23          14   \n",
       "4          34          24           3          22           4           3   \n",
       "\n",
       "   G20USSDBOO  G20USSRMEH  G20USSGHOF  G20USSOFER  G20USSOBUR  \\\n",
       "0         950        1057          14          31           4   \n",
       "1        1834        1907           7          14           7   \n",
       "2        1262        2014          17          37           7   \n",
       "3       12585       11533          97         295          73   \n",
       "4        7936        2161          42         145          29   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((372203.098 248033.314, 372179.200 24...  \n",
       "1  POLYGON ((489588.574 177413.205, 489558.719 17...  \n",
       "2  POLYGON ((427942.516 266334.807, 426253.899 26...  \n",
       "3  MULTIPOLYGON (((461924.197 182829.378, 461894....  \n",
       "4  POLYGON ((503633.330 186284.284, 503592.672 18...  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now, let's read in the voting data  \n",
    "VTDdbf = gpd.read_file(\"state_map_files/nj_vest_20.dbf\")\n",
    "VTDdbf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4f3b9ddb-b971-472c-a353-521377f95e46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CONGR_DIST</th>\n",
       "      <th>COUNTY</th>\n",
       "      <th>MUN_NAME</th>\n",
       "      <th>G20PREDBID</th>\n",
       "      <th>G20PRERTRU</th>\n",
       "      <th>G20PRELJOR</th>\n",
       "      <th>G20PREGHAW</th>\n",
       "      <th>G20PRECBLA</th>\n",
       "      <th>G20PRESLAR</th>\n",
       "      <th>G20PREOHAM</th>\n",
       "      <th>G20PREOFUE</th>\n",
       "      <th>G20USSDBOO</th>\n",
       "      <th>G20USSRMEH</th>\n",
       "      <th>G20USSGHOF</th>\n",
       "      <th>G20USSOFER</th>\n",
       "      <th>G20USSOBUR</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>02</td>\n",
       "      <td>Atlantic</td>\n",
       "      <td>Buena Borough</td>\n",
       "      <td>964</td>\n",
       "      <td>1140</td>\n",
       "      <td>15</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>950</td>\n",
       "      <td>1057</td>\n",
       "      <td>14</td>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "      <td>POLYGON ((-74.92508 39.51360, -74.92517 39.513...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>02</td>\n",
       "      <td>Atlantic</td>\n",
       "      <td>Margate City</td>\n",
       "      <td>1865</td>\n",
       "      <td>1945</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1834</td>\n",
       "      <td>1907</td>\n",
       "      <td>7</td>\n",
       "      <td>14</td>\n",
       "      <td>7</td>\n",
       "      <td>POLYGON ((-74.50897 39.32048, -74.50907 39.320...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>02</td>\n",
       "      <td>Atlantic</td>\n",
       "      <td>Mullica Township</td>\n",
       "      <td>1306</td>\n",
       "      <td>2085</td>\n",
       "      <td>25</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1262</td>\n",
       "      <td>2014</td>\n",
       "      <td>17</td>\n",
       "      <td>37</td>\n",
       "      <td>7</td>\n",
       "      <td>POLYGON ((-74.72767 39.56440, -74.73368 39.568...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>02</td>\n",
       "      <td>Atlantic</td>\n",
       "      <td>Egg Harbor Township</td>\n",
       "      <td>12986</td>\n",
       "      <td>11995</td>\n",
       "      <td>188</td>\n",
       "      <td>71</td>\n",
       "      <td>25</td>\n",
       "      <td>40</td>\n",
       "      <td>23</td>\n",
       "      <td>14</td>\n",
       "      <td>12585</td>\n",
       "      <td>11533</td>\n",
       "      <td>97</td>\n",
       "      <td>295</td>\n",
       "      <td>73</td>\n",
       "      <td>MULTIPOLYGON (((-74.60678 39.33530, -74.60689 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>02</td>\n",
       "      <td>Atlantic</td>\n",
       "      <td>Atlantic City</td>\n",
       "      <td>8148</td>\n",
       "      <td>2541</td>\n",
       "      <td>34</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>7936</td>\n",
       "      <td>2161</td>\n",
       "      <td>42</td>\n",
       "      <td>145</td>\n",
       "      <td>29</td>\n",
       "      <td>POLYGON ((-74.45930 39.34483, -74.45945 39.344...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  CONGR_DIST    COUNTY             MUN_NAME  G20PREDBID  G20PRERTRU  \\\n",
       "0         02  Atlantic        Buena Borough         964        1140   \n",
       "1         02  Atlantic         Margate City        1865        1945   \n",
       "2         02  Atlantic     Mullica Township        1306        2085   \n",
       "3         02  Atlantic  Egg Harbor Township       12986       11995   \n",
       "4         02  Atlantic        Atlantic City        8148        2541   \n",
       "\n",
       "   G20PRELJOR  G20PREGHAW  G20PRECBLA  G20PRESLAR  G20PREOHAM  G20PREOFUE  \\\n",
       "0          15           8           0           4           3           3   \n",
       "1          11           8           2           2           2           2   \n",
       "2          25           8           2           3           2           1   \n",
       "3         188          71          25          40          23          14   \n",
       "4          34          24           3          22           4           3   \n",
       "\n",
       "   G20USSDBOO  G20USSRMEH  G20USSGHOF  G20USSOFER  G20USSOBUR  \\\n",
       "0         950        1057          14          31           4   \n",
       "1        1834        1907           7          14           7   \n",
       "2        1262        2014          17          37           7   \n",
       "3       12585       11533          97         295          73   \n",
       "4        7936        2161          42         145          29   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((-74.92508 39.51360, -74.92517 39.513...  \n",
       "1  POLYGON ((-74.50897 39.32048, -74.50907 39.320...  \n",
       "2  POLYGON ((-74.72767 39.56440, -74.73368 39.568...  \n",
       "3  MULTIPOLYGON (((-74.60678 39.33530, -74.60689 ...  \n",
       "4  POLYGON ((-74.45930 39.34483, -74.45945 39.344...  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "VTDdbf = VTDdbf.to_crs(tractPopFile.crs)  #MD's precinct file in wrong CRS\n",
    "VTDdbf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "3fe4ceba-8077-4485-ad0a-f8f2a5755040",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "726 0.4192871641320516 4491609 = number of precincts, statewide GOP vote, total Trump+Biden votes\n"
     ]
    }
   ],
   "source": [
    "# Pull VTD geopandas data columns into arrays\n",
    "bigVTDGeom = VTDdbf['geometry'] \n",
    "# vtdGeom = VTDdbf['geometry'] #can't use VTDdbf because it uses alternate coordinate system\n",
    "bigVTDTrump = VTDdbf['G20PRERTRU']   #we will later convert to vtdTrump and vtdBiden after overlaying census data\n",
    "bigVTDBiden = VTDdbf['G20PREDBID']\n",
    "\n",
    "nPrecincts = len(bigVTDGeom)\n",
    "stateGOP = np.sum(bigVTDTrump)/(np.sum(bigVTDTrump) + np.sum(bigVTDBiden) ) \n",
    "print(nPrecincts, stateGOP, np.sum(bigVTDTrump) + np.sum(bigVTDBiden),\n",
    "      \"= number of precincts, statewide GOP vote, total Trump+Biden votes\" )\n",
    "#vtdTrump = vtdTrump.to_numpy()  #these two lines try to avoid pandas complaints when we overwrite precinct data\n",
    "#vtdBiden = vtdBiden.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "612322d1-382a-4bfe-9104-1a2d7bf2a9e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have read in  6599 worth of block group pop data\n"
     ]
    }
   ],
   "source": [
    "# Now, read in a block-group file as the basis for carving up the precincts by population\n",
    "block_dbf = gpd.read_file(\"state_map_files/nj_pl2020_bg.dbf\")\n",
    "block_dbf.head()\n",
    "blockGeom = block_dbf['geometry']\n",
    "blockVAP = block_dbf['P0030001']\n",
    "nBlockGroups = len(blockVAP)\n",
    "print(\"I have read in \",nBlockGroups,\"worth of block group pop data\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "963f1d28-deb5-4568-b243-9816b4075963",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on precinct number 0\n",
      "working on precinct number 50\n",
      "working on precinct number 100\n",
      "working on precinct number 150\n",
      "working on precinct number 200\n",
      "working on precinct number 250\n",
      "working on precinct number 300\n",
      "working on precinct number 350\n",
      "working on precinct number 400\n",
      "working on precinct number 450\n",
      "working on precinct number 500\n",
      "working on precinct number 550\n",
      "working on precinct number 600\n",
      "working on precinct number 650\n",
      "working on precinct number 700\n",
      "all done dividing 726 precincts into  14733  =  14733 precinct x block groups\n",
      "total VAP is now 7203027.392347396 was originally 7281310\n",
      "red-blue lean is now 0.4192871641320516 was originally 0.4192871641320516\n"
     ]
    }
   ],
   "source": [
    "#DIVIDE UP THE PRECINCTS INTO THEIR BLOCK SUBGROUPS #about 6min to run NJ\n",
    "nPrecincts = len(bigVTDGeom)\n",
    "dummyPoly = Polygon([(0,0),(1,0),(1,1)])\n",
    "vtdGeom = [dummyPoly]*1  #we don't know a priori how many precinct x blockgroups there will be\n",
    "vtdTrump = [-99999.]*1   # ... so we start arrays for these variables, and append as we go\n",
    "vtdBiden = [-99999.]*1\n",
    "vtdVAP = [-99999.]*1\n",
    "vtdPrecinctNo = [-99999]*1\n",
    "bigVTDnParts = [0]*nPrecincts  #this tracks how many block groups have at least some intersxn w each true precinct\n",
    "totalCounter = 0\n",
    "for p in range(nPrecincts):\n",
    "    if p%50 == 0:\n",
    "        print(\"working on precinct number\",p)\n",
    "    precinctVAP = 0.\n",
    "    overlapCounter = 0  #reset how many block-group overlaps for this precinct\n",
    "    for bg in range(nBlockGroups):\n",
    "        overlapPoly = blockGeom[bg].intersection(bigVTDGeom[p])\n",
    "        if overlapPoly.area > 0.:   #we have overlap of this precinct and block group\n",
    "            bigVTDnParts[p] +=1   #this precinct now has another block associated with it\n",
    "            fracArea = overlapPoly.area/blockGeom[bg].area\n",
    "            overlapVAP = fracArea*blockVAP[bg]\n",
    "            precinctVAP += overlapVAP   #running total of VAP contributions from intersecting block groups\n",
    "            if totalCounter == 0:\n",
    "                vtdGeom[0] = overlapPoly\n",
    "                vtdVAP[0] = overlapVAP\n",
    "                vtdPrecinctNo[0] = p   #which precinct no does this new sub-precinct belong to?\n",
    "            else:\n",
    "                vtdGeom.append(overlapPoly)\n",
    "                vtdTrump.append(0.)  #(fracArea * bigVTDTrump[p] )  #we assign these later after we know total VAP\n",
    "                vtdBiden.append(0.)  #(fracArea * bigVTDBiden[p] )\n",
    "                vtdVAP.append(overlapVAP)   \n",
    "                vtdPrecinctNo.append(p)\n",
    "            overlapCounter +=1\n",
    "            totalCounter +=1\n",
    "    # now assign sub-precinct Trump and Biden votes based on their fraction of this precinct's total VAP\n",
    "    for v in range(totalCounter-overlapCounter,totalCounter,1):\n",
    "        vtdTrump[v] = vtdVAP[v]/precinctVAP * bigVTDTrump[p]\n",
    "        vtdBiden[v] = vtdVAP[v]/precinctVAP * bigVTDBiden[p]\n",
    "            \n",
    "nBigPrecincts = nPrecincts   #this is the true number of precincts\n",
    "nPrecincts = len(vtdGeom)   #to be consistent with nomenclature in rest of code; these are subprecincts\n",
    "oldStateGOP = np.sum(bigVTDTrump)/(np.sum(bigVTDTrump) + np.sum(bigVTDBiden) ) \n",
    "stateGOP = np.sum(vtdTrump)/(np.sum(vtdTrump)+np.sum(vtdBiden))\n",
    "print(\"all done dividing\",nBigPrecincts,\"precincts into \",nPrecincts,\" = \",totalCounter,\"precinct x block groups\")\n",
    "print(\"total VAP is now\",np.sum(vtdVAP),\"was originally\",np.sum(blockVAP) )\n",
    "print(\"red-blue lean is now\",stateGOP,\"was originally\",oldStateGOP )\n",
    "            \n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "cffafc90-e2ed-4ad6-97b7-341c8ea21aa7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#What is our distribution of precinct's Trump votes by precinct Biden votes?\n",
    "fig, ax = plt.subplots()\n",
    "ax.set(xlabel=\"Biden votes in this precinct\", ylabel=\"Trump precinct votes\")\n",
    "plt.plot(vtdBiden, vtdTrump, marker='.',linestyle=\"none\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "c5c57f52-b8d2-4ef4-9d02-2429ed5e59da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ID the non-polygon PRECINCTS\n",
    "notPolyVTD = [0]*nPrecincts\n",
    "for p in range(nPrecincts):\n",
    "    if type(vtdGeom[p]) != type(vtdGeom[0]):\n",
    "        notPolyVTD[p] = 1\n",
    "        plotPoly(vtdGeom[p])\n",
    "        #plt.text(vtdGeom[p].centroid.x, vtdGeom[p].centroid.y,p)\n",
    "    else:\n",
    "        hi = \"hi\"\n",
    "        #x,y = vtdGeom[p].exterior.xy\n",
    "        #plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ea498c6a-d054-409e-92fb-cda921e2911f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#prep for geography triage\n",
    "#isSkippedTract = [0]*nTracts  #done above; already skipped two lakeshore tracts\n",
    "isSkippedPrecinct = [0]*nPrecincts  #will be used later"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "2018d6ad-9715-45b4-af84-47889b2ccbd0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on tract 200\n",
      "working on tract 400\n",
      "working on tract 600\n",
      "working on tract 800\n",
      "working on tract 1000\n",
      "working on tract 1200\n",
      "working on tract 1400\n",
      "working on tract 1600\n",
      "working on tract 1800\n",
      "working on tract 2000\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#build basic NJ map -- *** ONLY RUN THIS IF I FORGOT TO GENERATE WHOLE MAP IN FIRST LOOP\n",
    "wholeMAP = tractGeom[0] #uncomment for first time thru loop  **************\n",
    "for t in range(1,nTracts):\n",
    "    if t%200 == 0:\n",
    "        print(\"working on tract\",t)\n",
    "    wholeMAP = wholeMAP.union(tractGeom[t])  #uncomment to build map  ********\n",
    "for t in oceanTracts:  #subtract off these \n",
    "    wholeMAP = wholeMAP.difference(tractGeom[t])\n",
    "plotPoly(wholeMAP)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "3ded1b1f-5629-4d8f-826b-e037e5dcb3da",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on tract 200\n",
      "working on tract 400\n",
      "working on tract 600\n",
      "working on tract 800\n",
      "working on tract 1000\n",
      "working on tract 1200\n",
      "working on tract 1400\n",
      "working on tract 1600\n",
      "working on tract 1800\n",
      "working on tract 2000\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#build basic NJ map\n",
    "tractMAP = tractGeom[0] #uncomment for first time thru loop  **************\n",
    "for t in range(1,nTracts):\n",
    "    if t%200 == 0:\n",
    "        print(\"working on tract\",t)\n",
    "    if isSkippedTract[t] == 0:\n",
    "        uncomment = \"if-redone\"\n",
    "        tractMAP = tractMAP.union(tractGeom[t])  #uncomment to build map  ********\n",
    "x,y = tractMAP.exterior.xy\n",
    "plt.plot(x,y)\n",
    "pt1 = Point(-75.25,40.5)\n",
    "pt2 = Point(-75.08,40.55)\n",
    "pt3 = Point(-74.95,41.2)\n",
    "pt4 = Point(-75.25,41.2)\n",
    "clipPoly = Polygon([pt1,pt2,pt3,pt4])  #clip eastern outcropping NE of Philly\n",
    "xc,yc = clipPoly.exterior.xy\n",
    "plt.plot(xc,yc)\n",
    "\n",
    "plt.show()\n",
    "wholeMAP = tractMAP  #in case we later slice parts out\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "f33de347-5473-4b11-b3ae-86691b33098d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have flagged  15 tracts w pop 65955.0  to reassign to tracts...\n",
      "[631, 942, 1131, 1133, 1134, 1177, 1232, 1501, 1914, 1919, 2160]\n",
      "I have flagged  152 precincts to reassign to precincts...\n",
      "[7211, 7212, 7213, 7214, 7215, 7216, 7217, 7238, 7239, 7241, 7242, 7243, 7246, 7247, 7249, 7256, 7257, 7261, 7263, 7264, 7265, 7268, 7270, 7272, 7276, 7277, 7278, 7280, 7377, 7378, 7386, 7387, 7396, 7398, 7399, 7400, 7401, 7406, 7407, 7408, 7409, 7410, 7426, 7427, 7428, 7470, 7474, 7479, 7482, 7487, 13054, 13055, 13130, 13187, 13188, 13192, 13196, 13197, 13347, 13349, 13350, 13351, 13353, 13354, 13355, 13356, 13357, 13358, 13363, 13364, 13365, 13367, 13369, 13370, 13373, 13374, 13375, 13376, 13377, 13378, 13379, 13380, 13381, 13382, 13411, 13412, 13413, 13414, 13415, 13416, 13417, 13418, 13419, 13451, 13454, 13458, 13459, 13483, 13484, 13485, 13486, 13487, 13488, 13491, 13492, 13497, 13498, 13506, 13507, 13508, 13509, 13510, 13511, 13512, 13514, 13515, 13516, 13518, 13519, 13520, 13521, 13523, 13524, 13525, 13526, 13540, 13542, 13549, 13551, 13554, 13555, 13571, 13578, 13580, 13582, 13583]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#find all tracts and precincts centered in this clipPoly, flag for cutting\n",
    "# and, ID all nearby tracts and precincts to receive their pops and voters\n",
    "\n",
    "cutTractList = [-99999]\n",
    "cutPrecinctList = [-99999]\n",
    "tractReceivers = [-88888]\n",
    "precinctReceivers = [-88888]\n",
    "popToCut = 0.\n",
    "for t in range(nTracts):\n",
    "    x = tractGeom[t].centroid.x\n",
    "    y = tractGeom[t].centroid.y\n",
    "    if tractGeom[t].intersects(clipPoly) :\n",
    "        CP = Point(x,y)\n",
    "        if CP.intersects(clipPoly):\n",
    "            isSkippedTract[t] = 1  #uncomment once confirmed\n",
    "            popToCut += tractPop[t]\n",
    "            if cutTractList == [-99999]:\n",
    "                cutTractList = [t]\n",
    "            else:\n",
    "                cutTractList.append(t)\n",
    "            plotPoly(tractGeom[t])\n",
    "    else:\n",
    "        if tractGeom[t].distance(clipPoly) < 0.1 :  # and tractGeom[t].centroid.x > -76.4 :\n",
    "            if tractReceivers == [-88888]:\n",
    "                tractReceivers = [t]\n",
    "            else:\n",
    "                tractReceivers.append(t)\n",
    "xp,yp = clipPoly.exterior.xy\n",
    "plt.plot(xp,yp)\n",
    "\n",
    "for p in range(nPrecincts):\n",
    "    x = vtdGeom[p].centroid.x\n",
    "    y = vtdGeom[p].centroid.y\n",
    "    if vtdGeom[p].intersects(clipPoly) :\n",
    "        isSkippedPrecinct[p] = 1  #uncomment once confirmed\n",
    "        if cutPrecinctList == [-99999]:\n",
    "            cutPrecinctList = [p]\n",
    "        else:\n",
    "            cutPrecinctList.append(p)\n",
    "    else:\n",
    "        if vtdGeom[p].distance(clipPoly) < 0.1 : # and vtdGeom[p].centroid.x > -76.4 :\n",
    "            if precinctReceivers == [-88888]:\n",
    "                precinctReceivers = [p]\n",
    "            else:\n",
    "                precinctReceivers.append(p)\n",
    "print(\"I have flagged \",len(cutTractList),\"tracts w pop\",popToCut,\" to reassign to tracts...\")\n",
    "print(tractReceivers)\n",
    "print(\"I have flagged \",len(cutPrecinctList),\"precincts to reassign to precincts...\")\n",
    "print(precinctReceivers)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "33897c0d-3254-4a76-9b82-314a8f320eaa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 2 of 4\n",
    "#visualize the to-be-cut precincts, just to confirm there are really xx precincts up there\n",
    "for p in range(nPrecincts):\n",
    "    if isSkippedPrecinct[p] ==1:\n",
    "        plotPoly(vtdGeom[p])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "e2595c62-579a-4af1-9552-0859524782be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 3 of 4\n",
    "#now visualize the receivers\n",
    "for pp in range(len(precinctReceivers)):\n",
    "    p = precinctReceivers[pp]\n",
    "    plotPoly(vtdGeom[p])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "9e1d7fcd-cdeb-4317-9f89-5291a9595587",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#now visualize the TRACT receivers\n",
    "for tt in range(len(tractReceivers)):\n",
    "    t = tractReceivers[tt]\n",
    "    if notPoly[t]==1:\n",
    "        for geom in tractGeom[t].geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        x,y = tractGeom[t].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "d2622568-1fc9-4089-9ae4-2550ea6fbaaf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have finished slicing  65955.0 people (VAP of 52682.0 ) and  40428.6871994091 voters out.\n"
     ]
    }
   ],
   "source": [
    "#now, cut and redistribute\n",
    "cutPop = 0.\n",
    "cutVAP = 0.\n",
    "cutHisp = 0.\n",
    "cutBlack = 0.\n",
    "cutTrump = 0.\n",
    "cutBiden = 0.\n",
    "\n",
    "for ct in range(len(cutTractList)):\n",
    "    t = cutTractList[ct]\n",
    "    cutPop += tractPop[t]  #sum up the cut pops\n",
    "    cutVAP += tractVAP[t]\n",
    "    cutHisp += tractHisp[t]\n",
    "    cutBlack += tractBlack[t]\n",
    "    # Now we zero out the pops in the cut tracts\n",
    "    tractPop[t] = 0\n",
    "    tractVAP[t] = 0\n",
    "    tractHisp[t] = 0\n",
    "    tractBlack[t] = 0\n",
    "# Now distribute the pops among the receivers\n",
    "ntR = len(tractReceivers)\n",
    "NTR = float(ntR)\n",
    "for rt in range(ntR) :\n",
    "    t = tractReceivers[rt]\n",
    "    tractPop[t] += cutPop/NTR\n",
    "    tractVAP[t] += cutVAP/NTR\n",
    "    tractHisp[t] += cutHisp/NTR\n",
    "    tractBlack[t] += cutBlack/NTR\n",
    "\n",
    "#             now do the same for the precincts\n",
    "for cp in range(len(cutPrecinctList)):\n",
    "    p = cutPrecinctList[cp]\n",
    "    cutTrump += vtdTrump[p]\n",
    "    cutBiden += vtdBiden[p]\n",
    "        # Now we zero out the pops in the cut precincts\n",
    "    vtdTrump[p] = 0\n",
    "    vtdBiden[p] = 0\n",
    "\n",
    "# Now distribute the pops among the receivers\n",
    "npR = len(precinctReceivers)\n",
    "NPR = float(npR)\n",
    "for rp in range(npR) :\n",
    "    p = precinctReceivers[rp]\n",
    "    vtdTrump[p] += cutTrump/NPR\n",
    "    vtdBiden[p] += cutBiden/NPR\n",
    "print(\"I have finished slicing \",cutPop,\"people (VAP of\",cutVAP,\") and \",cutBiden+cutTrump,\"voters out.\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "fe47f683-719d-4a02-9648-905bddbf28a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#now work on the southeast cape\n",
    "plotPoly(tractMAP)\n",
    "pt1 = Point(-74.6,39.2)\n",
    "pt2 = Point(-74.98, 39.15)\n",
    "pt3 = Point(-75.1,38.8)\n",
    "pt4 = Point(-74.7,38.8)\n",
    "clipPoly = Polygon([pt1,pt2,pt3,pt4])\n",
    "plotPoly(clipPoly)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "cac3531a-35c7-438d-b5b9-5fcdf0745329",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have flagged  22 tracts w pop 67339.0  to reassign to tracts...\n",
      "[81, 335, 346, 347, 541, 564, 579, 818, 821, 1369, 1524, 1700, 1702, 1704, 1705, 1708, 1710]\n",
      "I have flagged  181 precincts to reassign to precincts...\n",
      "[44, 46, 80, 84, 92, 159, 162, 289, 293, 3922, 3923, 3925, 3926, 3928, 3929, 3930, 3931, 3932, 3935, 3936, 3937, 3938, 3939, 3941, 3943, 3944, 3945, 3948, 3949, 3950, 3951, 3952, 3953, 3954, 3955, 3956, 3957, 3958, 3959, 3960, 3961, 3962, 3963, 3965, 3969, 3972, 3974, 3976, 3978, 3979, 3981, 3983, 3984, 3985, 3986, 3988, 3989, 3990, 3991, 3992, 3993, 3994, 3995, 3996, 3997, 3998, 3999, 4006, 4007, 4008, 4009, 4010, 4011, 4012, 4013, 4014, 4042, 4171, 4173, 4277, 4278, 4280, 4284, 4286, 4287]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#find all tracts and precincts centered in this clipPoly, flag for cutting\n",
    "# and, ID all nearby tracts and precincts to receive their pops and voters\n",
    "cutTractList = [-99999]\n",
    "cutPrecinctList = [-99999]\n",
    "tractReceivers = [-88888]\n",
    "precinctReceivers = [-88888]\n",
    "popToCut = 0.\n",
    "for t in range(nTracts):\n",
    "    x = tractGeom[t].centroid.x\n",
    "    y = tractGeom[t].centroid.y\n",
    "    if tractGeom[t].intersects(clipPoly) :\n",
    "        CP = Point(x,y)\n",
    "        if CP.intersects(clipPoly):\n",
    "            isSkippedTract[t] = 1\n",
    "            popToCut += tractPop[t]\n",
    "            if cutTractList == [-99999]:\n",
    "                cutTractList = [t]\n",
    "            else:\n",
    "                cutTractList.append(t)\n",
    "            plotPoly(tractGeom[t])\n",
    "    else:\n",
    "        if tractGeom[t].distance(clipPoly) < 0.1 :# and tractGeom[t].centroid.y > 48.3:\n",
    "            if tractReceivers == [-88888]:\n",
    "                tractReceivers = [t]\n",
    "            else:\n",
    "                tractReceivers.append(t)\n",
    "xp,yp = clipPoly.exterior.xy\n",
    "plt.plot(xp,yp)\n",
    "\n",
    "for p in range(nPrecincts):\n",
    "    x = vtdGeom[p].centroid.x\n",
    "    y = vtdGeom[p].centroid.y\n",
    "    if vtdGeom[p].intersects(clipPoly) :\n",
    "        isSkippedPrecinct[p] = 1\n",
    "        if cutPrecinctList == [-99999]:\n",
    "            cutPrecinctList = [p]\n",
    "        else:\n",
    "            cutPrecinctList.append(p)\n",
    "    else:\n",
    "        if vtdGeom[p].distance(clipPoly) < 0.1: # and vtdGeom[p].centroid.y > 48.3 :\n",
    "            if precinctReceivers == [-88888]:\n",
    "                precinctReceivers = [p]\n",
    "            else:\n",
    "                precinctReceivers.append(p)\n",
    "print(\"I have flagged \",len(cutTractList),\"tracts w pop\",popToCut,\" to reassign to tracts...\")\n",
    "print(tractReceivers)\n",
    "print(\"I have flagged \",len(cutPrecinctList),\"precincts to reassign to precincts...\")\n",
    "print(precinctReceivers)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "d7009c9a-d5ff-4570-855d-1fb439afc32c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 2 of 4\n",
    "#visualize the to-be-cut precincts, just to confirm there are really xx precincts up there\n",
    "for p in range(nPrecincts):\n",
    "    if isSkippedPrecinct[p] ==1 and vtdGeom[p].centroid.y < 40:\n",
    "        plotPoly(vtdGeom[p])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "0218d8e6-7832-479f-b44f-b37c117ee40c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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tQ/uMMjIFISsAmRvkHDlCwqTJ5J48iSYgAN8pk3Ho3v2hjPb9N6mLl6D0cMexZ8/yFkWmDAlLD+PbI98C8GPHH9EqH66UJoVBVgAy6ENDSZgyhezde1B5elLpswk4DxiApFaXt2iljtVgIPfECVyGDkVSyX8OjxJfHvySM0lnyDXn8tu533ip/kvlLVKZI3/iH2GMEREk/jiVjPXrUTg54TnmPVyeeQaFTlfeopUZ5vh4hMmExr9yeYsiU4YcjD3I4bjDfND0A47GH2XWmVk8FvQYlewqVqnS0ubh3tvLFIgpPoHYT8dztXcfMnfswO3VV6i2ZTNuL7zwSC3+AGpfX5ROTuQcOVLeosiUEUIIph2fhpetFwODB/JeyHtYrBamHJtS3qKVOfIO4BHCkp5O8uzZpCxchLBYcBk0CPfXXkXl8egmPpOUStCoQaEsb1FkyohdUbs4nXSaT1t+ilapxc/Bj+fqPsfM0zMZXHMwjTwblbeIZYa8A3gEsObkkPTrTK506Ury7Dk4dOtK0Pp1VPrk40d68f8HkZOL0rFiFqqXKVmswsr0E9Op7FCZftX63bj+Qt0X8LL14utDX2OxWspRwrJFVgAPMcJoJOWPP7jSvTuJU6ZgGxJClb//xvfbb9FUlm3eAKaEBKzZ2ah9/cpbFJkyYHPEZi6mXuT1hq+jVtx0crBV2/JeyHuEpoSy8srKcpSwbLmnApAkSSdJ0mFJkk5JknROkqQJ+dcbSJJ0QJKkM5IkrZEkybGwffPvjZckKVqSpJP5X71K9tEeXYTVSvqatVzt3Yf4zz5H4x9AwB+/U3nGz+iCH64I3vslefZsAOxatypnSWRKG7PVzE8nfqKaczV6Bt7u8ts9sDtNvJow9fhU0g0PfnLDwlCYHYAB6CSEaAA0BHpIktQCmA2MFULUA1YCY4rQ9x+mCCEa5n+tv4/nkPkX8V98QcyYMShsban86y8ELFqIbePG5S1WhSR7/37sWrVEV7NmeYsi8y/MVjPJucmYrSWXZnxt2FrCM8J5o+EbKAs485EkibHNxpJuTGfGqRklNm9F5p4KQOSRlf+jOv9LAMHA7vzrW4AnitBXppQwXLlC6h+LcX7qKaqs/Av79u3l6NY7YIqNxXg1DBs5+VuFwWQ18eXBL+m0rBMdlnWg5R8teXHzi5xNOnt/41pM/HLqF2q71aaT/53TfdR0rcnAGgNZcmEJl1Mv39ecDwKFOgOQJEkpSdJJIAHYIoQ4BJwF+uY3GQgUaFS+Q99/eEOSpNOSJM2VJMnlDv1fliTpqCRJRxMTEwv1UI8y/1S0cuzx8Efw3i+Z27eDEHIEcAUiKjOKJReXUMOlBh80/YAnazzJtbRrDNswjF9P/YrJUrzqcysuryA6K5o3G715zxeiNxq+gYPGgY/3fYzJ+vBUuyuIQq0QQgiLEKIh4Ac0kySpLjACGClJ0jHAATAWoS/ADCCIPNNQLDDpDv1nCiFChBAhHrLHyh0RQhA1ajRJU6cBYM3NLWeJKj7606dRurigCQwsb1Fk8rmeeR2AJ4OfZGjtoXzQ7AP+6vcXnSp3YvrJ6QxaO4iTCSeLNGauOZeZp2fS2LMxrX1a37O9s86ZT1t+yrnkc/xy6pfiPMYDQ5FeEYUQacBOoIcQ4oIQopsQogmwGLha2L75P8fnKwcrMAtoVlThZf6FxUL2/v03frRv164chan4WI1GMjZsxK5lS9lEVkHIMmbx5cEvqexQmXa+Nz+/TlonJnWYxPRO08kyZTF8w3C+OPhFoQ9ql15YSmJuYqHe/v+hS0AX+lfrz+wzszmRcKJYz/MgUBgvIA9Jkpzzv7cBugAXJEnyzL+mAP4H3KYq79Q3/2fvfzUdQJ5JSaaYSCoVAb8tAMC+c2c5r809sKSkIIxGbBo9OkE/FZ2vD39NXE4cX7X5Clu17W3321duz6p+q3im1jMsv7Scbn92Y83VNXcdM8OYwZyzc2jl04qQSkU76xnbbCzedt58uOdDsoxZ9+7wAFKYHYA3sEOSpNPAEfLs+GuBpyVJukTegh4DzAOQJMlHkqT19+gL8G2+C+lpoCPwdok91SNI6pIlXBs4CABNYEA5S1PxsaSlAaCwebRSX1RUtkRsYfXV1bxU7yUaeja8YztbtS0fNPuAPx/7k9putflo70fsiNxRYFshBN8e/pY0QxqjG48uskx2aju+bvs1sdmxTDw8scj9HwiEEA/MV5MmTYTM7VjNZhHasJG49swzIvfCRWG1WstbpAqPMTpanA+uKRKmTitvUR55rFar6LSskxi0ZpAwWoyF7pdjyhEDVw8UrRe3FvHZ8bfdn3V6lqg7v66Ydvz+/o9/PPajqDu/rtgesf2+xilPgKOigDVVdhN5GFAoELm52NSrjy64hmzTLgSZ27YDYN+xYzlLIhObHUtCTgKPV3v8lujce2GjsmFiu4kYzAY+3vcxVmG9cW9T+CZ+PP4jPav0ZGTDkfcl32sNXiPYJZgJByaQqk+9r7EqGrICeMARQpAwMW97qvZ+tFLZ3g+ZW7ag8vZGV7tWeYvyyPOPj38d9zpF7lvVqSrvhbzH/pj9LL6wGIBTiaf4aO9HNPJsxOetP7/vFyKFpKBLQBeS9cl8eejL+xqroiGfFD7gRA5/9kYqY5chQ8pZmgcHtZ8fhosXwWwGjaa8xXmkOZd8DpVCRQ2X4qUpGRQ8iN3Ru5lybApapZZpJ6bhYePBDx1/KJEqXzNOzeDX078CeTuLzv6d6Vnl4YgdkXcADzj2HTrc+P76a6+TZ+6TuRe2ISFY0tMxXAsvb1EeaYQQrLm6hqpOVdEoi6eIJUliQqu8NGMTDkxAo9TwU5efcNW5FmmclZdX0n5pex5b+RjNfm9Gx2Ud6bGiBzNPz7yl3c7rO4slZ0VE3gE84Li9MALXYUNJ/Plnkn/5ldxjx7CVUxsUGoWtTXmL8EiTZkgjMTeRxNxEckw5Bbp/FgZ3G3dmd5vN0fijDKk5pNDjpOpTWRe2jtCUUFZfXU19j/q46lxp7t0cg8WA0WKkb1BfOvt3ZsSmEdRxq8PHLT4ulowVEVkBPARIGg22TZuS/MuvWLIeTn/lkkahyzMNCL2+nCV5tJG4aZ83WAzFVgAADT0b3tWF9L/MPzufn07+hN6iR6PQMKjGIMY2H3vHg+gdg3agVqgfKicLWQE8JOQeOw6ArRzYVCj+SZUhm8zKl39y7ztoHHDRFZgOrFQ4Fn+MSccm0aFyB0Y3Hk2Qc9A9+xTXRFWRkc8AHhJyT50CIO6LL0lfu66cpan4WPPf/JVOTuUsyaPN0FpDkZDINeey9MLSMku+duZ6nuPEmAaFW/wfVmQF8JDg8c7b6OrWJXPzZmLee4/Yjz/Bmp1d3mJVWFQueW+b6Sv/Ll9BHnHUSjWbn9yMk8aJLw59wfu73i/1Obd/9CLTT0xHYRWYU1JKfb6KjKwAHhJs6tShyp/LCT5yGKd+fUlbvpz0dfJO4E7Yd+6MTZMmJM+adSMthEz5UMmuEkv7LKVrQFd2XN9R6ma5peYDqCwwQzGcqtWblupcFR35DOAhQwCWtHRQKNDVKXpgzaNC6sWL5NjZIWVnk7l9B86PDyhvkYpMjtHMqpMxnI1OJ9dowclWjaeDDl8XG5oEuODr/OB4OHnZeQF5dnarsKKUlFisFszCXCK+/P/makNPPM0KWg0r/d1GRUdWAA8RwmIh+s23yNq1i0rjx2MjK4ACSY+OJnzECGyz8kxk1zZsoNEDqADG/XWGv0/G4KBT4ahTk55rIsuQV0Kxrq8ja99sW84SFp4rqVfYErGFl+q9hEJSsObqGiYfm0yKPoWxzcbydM2nS2SetISzxBuTSmSshwFZATxE5Bw5QtauXXiOeQ+XwU+VtzgVlktfT8Q2OwfjO29z5exZUjw8eNB8pxIzDaw7E8uwFgFM6FsHhSLPNTHLYKbDdzvQm6ysOhmNj7MNdXwcsdVU7D/1U4l5TgyNPBvx4d4PWRe2jgYeDfCx9+HbI9/iZ+9HW7/7V2ir/nwK7BR8W/+N+x7rYUA+A3iIUDo7A5B78iQZGzdiuHxZdnP8D0IIlIcOkVG1Kg1efhl1ly6kaDQP3O9p2dHrmCyC51oH3lj8Aew0SrydbLiSkMWoJScZ+MsB3lpc8QuaNPFqglap5fVtr7Ph2gZGNhzJgh4L+Lnzz1R3rs7oHaPZFrHtvr2EMix6JCEISIsrIckfbGQF8BChq1kTj9Gjydy+g+jRbxP2WF+iRr6B1WAob9EqDImHD2OTmYmmXd7bpIeHB7m5uaxcuZKLFy9iNBZY2bRCYbZY+f1gBK2C3AjysL/lniRJrHmzDecmdGfrO+3wcNCiVSvLSdLCE+gUyOLei3mnyTv81fcvXm3wKkqFEietEzO7zqSqc1VG7xxNyKIQBqwaUOx0DIP7zMHDYuH1sKWYUq/ds/3ZpLMk5T68JiPpQXrzCQkJEUePHi1vMSo81uxsjJGRZO3cSeKPU/EYPRr3V18pb7HKHavVyuEhz+Bw6hSV1q/DrUoVcnNzWbNmDVeuXMFoNKJSqahXrx5dunTBzs6uvEW+DatVsPhIJB+tPMvMYU3oVufOGWDNFivBH2/Ew15LjUoO6FQKtGolOpWC6l72VPO0p66vE54OFb8ojsFiYHP4ZiIyItgWuY2w9DCW9F5CLbfCZ3N9a/VCtsYuQqmNQSus7H5sJbbud05AF5YeRr+/+wHwbO1neb7u87jZuN33s5QHkiQdE0LcliOmYhsGZYqFws4OXa1a6GrVInP7DjK3bJEVAHD6yy9xOnmSrN69cKtSBQAbGxsGDRqE2WwmIiKCs2fPcvr0aUJDQ/H398fOzo7s7GyUSiXdu3dHpVJhb29/j5lKnpPX05i0+SIHw5IxWQT+rrZ0ruV11z5KhcSgED+uJmaTnmsiwWTBYLaSYzSz/FjUjXbLXmlJsypFS5xW1miVWh4LegyAgTUG0uXPLhyJO1JoBRCRksb25MlIkjO2xtZM6T3iros/wIZrGwBo7duahaELWXZpGU/XfJrn6jxXplHLpYmsAB5ShNVKyrx56M+cwal///IWp9wJX7ECze9/kFIzmBbffHPbfZVKRVBQEEFBQTRp0oT9+/eTlJREdHQ0RqMRk8lEaGgoAF5eXvTp04fKlSuXutxhiVnM2hPG4sPXcbPT8HzrKvg46WhVzR2l4u45aSRJ4uvH6xd4LyYtl4txmTw//wiDfj3AgQ874e30YLiNRmXlKa9KdoWvf5Geq0dSmKmv68Afwz+9a9sMYwZKSUm2KRsblQ2/dPmFa+nX+OXUL8w7O4+lF5fyR+8/qOpU9b6eoyIgK4CHlNgPPyR91WocunXD66Nx5S1OuZJy8iSZ4yeQ7e5OgzlzUKru/rH38/Nj0KBBt1wLCwvj/PnzJCUlER4ezvLly3nppZdwcHAoNbmvJGTSZfJuAF5uV5W3OlfHXlsyf7I+zjb4ONswtIU/iw5G8ti0fewa0wG7Io5vslhRSNINZaQ3WVh+9DqXE7LYcTGBkR2qMbiZ/239EjL1SEh4OBTdx39X1C5UChWtfFoVuk9930rYGp0wpl2/YxursDLp6CQWnl+IIM807mnjCUAVpyp80+4bHDWOLLu0rMRjE8oLWQE8hJhTUkhftRrnQYOoNGH8Q5W9sCgIITi/YgWGryeCSoXv9OnYuxXPhlu1alWqVs174wsPD+f3339n9uzZ9OnTh6CgIBSKkvenOBudAcAHPWryWofSyVfzRf961PFx4sO/ztBp0k42jW6Hs23hkp5diMvgublHUKskHLRqErMMKCSIz8hzOnDQqhj71xnm7QsnU29i5vAQvJ10TN5yid8PRQLwXKtAPulT+xZPpntxNO4oDTwaYK8pminOzuhIrjqT7HQDdk63LuBGi5EJByaw+upq+gX1I8g5iBR9Cr2q9LrRJseUw/pr6+lUuRO+9r5FmruiIiuAhxBJoQCVChTSI7v4p8fEcGbMGNyOHUdycMBjymT8GjYokbEDAwN5/vnnWbx4Mb///jvBwcE8/XTJBCr9m5j0vIylTzQp3cVmcNPKnI1O5/dDkXSdspsaXvYkZRr5bmB96vs5YzBbCEvM5mx0Ok42ahx0an47EM6W8/HYqJVIRriekieru72WBSOa0SzQFYsQvPHHcZKzjMSk63nt92Ok5ZjIMVoY1iIAs9XK/P3hNKviSq963oWWNzormo6Vi17L2d7qRKY6jbiwdIIaed64fjTuKP/b9z+is6IZ2XAkr9R/pcC/mzVX15BhzGBY7WGYTSa2LZyDKTMDrY0tnlWCqNWmPRqb4qezLg9kBfAQonR2xvmJJ0hb8Rduzz2HJjAQAGNUNNaMdLS1aj20ikEIwZFXf8Y+/ASukSfI7daNel9+gbqETTU+Pj68+eabbNmyhSNHjnDt2jWq5B8sF4WlI77GN/4kgfOm4efpeeMZJEnibHQ6ABdiM0vVU0eSJL4cUI+alRzYdC6evVfy3B6fmLGfxv4unIhMw2ix3tLHUadiRJsqvNimCnZaFWm5JhQSOOjUt5ip5j/fDIDFhyOZtPkirYPcea97Dap5OmA0W1l8+DpXEgquYbH9QjznojNQqxS42mmo6+NEdS97nLXOJOuTi/ycbjo34okkN+Omq+/1jOu8tvU1KtlVYmbXmbT0aVlg3wxjBl8c+gKA9PR0FqycTdqOvFxbOgdHTm/byIE//6DXm2Pwr1vwuUtFRFYADynur79OxoYNRL3zDl5jx5Jz8CDJs2YjTCac+vXD55uJ5S1iqaDPNeDjUh9c6hPbtSZN33uv1ObSaDR06NCBK1eusHjxYoYOHYq3tzc7t/3IsktLwW44PT2NtKjSn0xzGp6BVbC3s0Gl1PHjh09AtJnE7u7Y7m3BB8t3MWjPYtTOdXAXDmRZnBnsMw2vJiGkhI5n+R+vM3D886X2LADDWgYyrGUgZ6LSWXM6hgX7wzkWkcqwlgE09nch0M2OTL2JpGwjnWp63rLQ3+vs4Olm/jz9n7MAtVLC1U7DpnNx1PFxpGOw5w1T0P4rSYyYf7vLt0alwNO9PynOO24oysLionVFb8nCYrnp+j7v3DwswsKsbrPueqicYcgzx1XNqMqhlYewmC1olCoUFjOPfTAelbCy6ZcfWfHVx/R4bTS12hZ9h1IeyHEADzFZu3YR9dYohMEAkoR9u3YYwsKwpKYSfPRIeYtXKiQfPEvSW6O57GrDFQcj7cZ8RONm7Up1zoyMDObPn09OTg62trZcMe5mh8dF7NP6MOR8ApeD3Oh0NYDrHnNIrN+c6hvtUbZIIH57OlInW45omjLAfxHV133E1ujf0dlb0Hi1oI5qLjXJ5kR7HUnrP8ThzDla/fEpzm7Opfo8/xCfoccqRKl6B/15LIpPVp0lx2ihvp8Tb3WqTvtgD75Ye56lR69z+KMuqBUKYtJzOReTwfGIVJYcuYbeJOHsHM/n/erxWK3CZfR8b+5nbFIuZ1nwJmq18CEpN4nuf3anb7W+fNry7p5BFouFP/7oiL29kdDQtrz00vuYsrP448NR6DwqMfLHXzHm5rDquy+4fv4M7YeOIOSxx0viV1Qi3CkOQI4Efoixb9+eoC2b8ftlBkFbtuDx7juYoqNx6tu3vEUrNeL/3oU1IxqFjTNGlYq1P33F8WmrS3VOR0dHhg4diqOjIykpKXSoOoTn9Y8zzKRAgURCgBOZOTkc0XbFOUGF0WqDvlIEXoHteOvFRXzbpQeex95D082NnifPc9j7MjGNtUTXeIUF7X6jerUjJGVVpVLcYd6e3Johfz5Tqs/zD16OulJ3DX2yiR8nPunKlKcaEJ+h58XfjtLx+538fiiSDjU8cdSpsdEoCfKwp28DH8b3rcPhcV3p08xARqYTby6Ipcsv0zmXeOXek5nydgvKfGvaqiurMFqNPFv72bt2u34xlHPdauPjG42jUyL1G2wHUvEJrEJgq44Y4mPY9fcKtLZ2PD7uM2q0aMOuRXPZ+dtshNV617HLm3vuACRJ0gG7AS15JqM/hRCfSpLUAPgFsAfCgWeEEBmF6Zt/zxVYCgTm9x8khEi9myzyDuD+iPnoIzI3bqLatq038gY9bBwd9jZ2Rzbi+OV0dl0/QeqRYxjN2dRyaUnHSaPR2pXegmaxWMjNzb1roNgvX43APeMidUbNp5Z39dvuz5nVicT4Koz5cBZK5c33M6vVyvafZvCu/QysCokzz54plWcoT0wWK9tCE5ix6yoRydkseqE5dX3vXLEtKi2N15du5vQ1OyRVGp0ap/BD7+dw0BZ83vPR3G9ZrVzI7IDlNGsfzIBVA3DUOvJbz9/uOMeGrdsIfOMNMh4zk9XdipvDO8SnT8Nisad9u82oVXZMf+05hMnIqz/Nw87REWG1smPBLE5sXEOd9p3p9spbKJRFS8dhyTZhSdGjdNWhtCu4RnFRuNMOoDAKQALshBBZkiSpgb3AKGAa8J4QYpckSSOAKkKIjwvTVwhxUJKkb4EUIcRESZLGAi5CiA/uJousAO6PiGHDESYTgUsWA3kpI8yJiaj9/fM8hx4CDnR9Aseo8wQfPY7KzobUsFi2fDGJ69kXcNV4027EqwR1bFLeYhYLi8XCt5OfpGX2dbQDZ7Jv5Xbat2lP0w4PX1GTotj3t18K570/j5KSYYOzSxTb33wGV9vblfDcLUuYEvMlL8ZMoMvLwQxeO5hPWn7CwBoDCxz3tQ+mM2LtbOwsehI/MaEWTrR65jihoauIjnkHYe1M164zObZzGztnTMGjQQjDx42/If/Bv5awf9nvVG3clJ5vvIvO7t5uq4bwdDK2RmK4mkZ+KAK62m64DqyBwqb4R7bFNgGJPP45plfnfwkgmLy3e4AtwBNF6AvQD1iQ//0CoH+hnkSm2Ohq1yL39Gniv/6aqDff4lLLVlzt0ZNrAx7HnHrXzdcDg5SdjF6rQ5X/pu9S1ZuBc76jWc3HyTKnsubXz1n14idc3nConCUtOgs/Wkh1dQeO1WnOgAQFsUoLx7bvY8LEdzm0bQnhx7bcaHs5IY7+mxYzcd4sLhy6VKR5KsK5YFEOdzvVCOTwB48zqJWKtFQ/Xv/z7wLbVfHMO4SOTo/it0N/oFFo6BbQ7bZ2ZrOR5Kd9eGP1T9hbDJgCBOZK4GHfCYBatfphMoUAO0lODqNJh87YV6lOwqljXD598ob8LZ94ms4vvE74qeMsGjuKy4f2Y7VaCpTNGJtN0m/nSfzlNKaEHBw6+eM2tBYOHSujD00maf45hLXk/18KdQgsSZISOAZUA34SQnwgSdJ+4BshxCpJkt4BJgghbtt7FdQ3/3qaEML5X+1ShRC3JdiQJOll4GUAf3//JhEREcV4TBkAc2oqseM+ImvXLpQODjj264vG15f47yfhMmgQlT7+X3mLeN8cbdgYs409LQ7svu1e5L6zHJm7iMis84CZei5xXGjwJC+NGI1WW/H9t396dTsqczRNfcbRu+lffL9jJ1lJ55EMrlxv+xefJ6Vw4oXL6K/uY9Lej/HSNCeqeku+POxHeNY1+kx7rcBxd4x+l/TTG3BT2BPqWJ36nMHB7yXqTH0TgOjoGHa9+y71ojK4qM3gia27S9yNOD0rDSd7ZwCsFguHlizFxs6AJseEOj2b3CvnyQ4OpPmLH9416K7F5LkkJDtwbnxfbNS3BntdT4il14ZutAzrz4GqfwOwb+BeHG1vmplSo86SM6IPGZE22PvmkhVnT/rjBrLbWGnddDc617yYjNjY05w99zgWS0t6dF9IUmws8999DbWTM2/+NPcWGaMvhrL5lx9JiYnCxsER98oB2Dg5IwE56WnUyGqAu9IXSavEoZ0f9m19UWjyTEaWLCOxX+S9rFT6sBkqp+JFIBfbBPSfQZyBlcCbgBmYCrgBq4G3hBB3DLP8d18hxNnCKoB/I5uASgZhtd5i8oka/Ta5J05QfdfO8hOqBDDr9Vxo1JhM32Babl15x3YxJy5zesbTXMjwxCIUKNQWbNz12Hvn0vaJ7wioVjEraW3cOJuDG2egcWzBB59OQ6lUsOG3WUywzqR/ZmtyshTMbTaED38/gm9iHBt6XqGV2yCaHYZtKZGMnPvGbQv3sVVbER+/g43JitHBSqzGGx9jPNpMiAhuwfG2gfT6+w+0iXnrhEWS8NizEy93zwIkLDqJ1+NJmPIqbvb7OODSlH6j1pDy0au4aZbyXx0jrJCAA86fRqJVFqwEvt62mV+3GPlykAvPNG59yz2r1UrjBSHUjKnNOb+8AjQ2JhXtbEII8WuOautqGm8+jyFDhUe9TETnT0j8cQrxX5uwy/Ch2bA9t4y3afMIlMrdVK/2GwEBrVg95xcub15L3b4D6f7MrQfLVouFy4cPcO3EEVLjYsnNyIvxsHF0olluF6zOgqqjOqK0vxmFbYzOIvm381iyTbg+WR3bhsX/nZeIAsgf6FMgWwjx/b+u1QAWCSGaFbavJEkXgQ5CiFhJkryBnUKI4Lv1lxVAySOE4NoTTyAMRoLWrS1vce6L2A17SXv7JbKadKXp71Pv2X719r85c2QRfvoYcpJsyEnUoXEw0erZvjRp82BVjErNSKbWsesMMi2m2e/Z2DrW5fFprwJw6J22BEZGsl81nKR6BirXaMymIxcYEL0a+8tWbLIsXOk5jMcm5dXIPb16D+bxH2CTk2cWjPF2x9inNX6//U2KvZJ2e88iSRJHdi4mbPafVHvyDZr070jEgWMcO7OK3s+PQ6suXODatleb0LnSFXKsWiSVlX3Br9ExdDop1uoczXLHW0g4IkHcNdx947C3s/Br6194rdvgW8ZJir9CzM9PYtGo8JGSUQkLGk0WWXYqjiW0plnrMXh2bUX7WV1xTLXS8YQbwSMfZ97ZuVzUxdL4ipXRq6zYYsWuszt+P+zjUqvWZFVOIvUVM9V5E/9Oo2+ZMz09hoOHOmM2V6Z3r81YzGamvvYclpxsXvxxFs7u7vd8/rQrUWTNvkZmQBa1Xut543rOqQRS/7yMwlaF27DaaPzuL5Cx2OmgJUnyAExCiDRJkmyALsA3kiR5CiESJElSAP8jzyOoUH3zb68GngUm5v+7qpjPJlNMhBDETZiA4Xwo3l9+Ud7i3DfJew6jBBxqF64Wct9O/enbqf+Nn3eu/YBjC8+xf/5qgus/ib1j4bNNljcujm4cdIsldXdPKn9dG3efm2+L9tY0Us7aUpM/4STAGl4Gkj0cMTsZMHcczWPfvHCjff2+bdlr/ypbl04kw34QH345lh/+3kWgYRUnG7WivSQRm5JC+vszqJ+RSFrkV6zdupOAXauoZTKwZ+1y2v11Fo3qVs8XIQRHhj1DbecdXPGW2G7pzWu+kcRnVyatRTtqnPwdh9DdKLFyROFNr8m3LgnHv25CbVM4/WvXvuX6xcM7YfUg6qsMRBvc2O3TmFxJRy/9JlxSzfRhO2LvDnJ2t+EJNx/+0p5FZ1+V3m0G07JuT/YO7EL161kIVxOuY9/Bo+/rZB04gCUlhdyBFhSZCnx7vXrb79zJyQcbm4GYTL9z+vRi6td/mq4vvcHGSV+wYup3vPDZ7Vln/0vMhjPYC1u8utXL+x1ZBRmbw8ncGYUm0BG3Z2qhdChcbqbiUJhjZW9gQb4tXwEsE0KslSRplCRJI/Pb/AXMA5AkyQeYLYTodae++X0mAsskSXoBiAQKPoqXKTUyN28hbclSXJ9/HqfHK07QSnHJuXQBB8C9U8Hh/PeiQ59vyEgawuUNGfw5+QmGf7KnVJK8lRaB9esSWEAWglTREVe2ENfzGS46h9H52j7ihs6hTadWd3y+Np2G06bT8Bs/v9G3FbtmB9Dx2AHWvjYWddQO/DMySLZ3wik5DuOpHRhVCuJc3PC7lMyPf/zKmOGv3zLmrtcb0aHaNTLMnnibM3hHLCNa68Flx6607zcZ04m11BM5oACVg8ctfY2Z2QQaEomgMsF+tz5kzvK5NLQ18KutKy+/d4WB/34mIQhduQTf41uxVazlrTQ9TyZXJjXBhvU92uAVk0l1o5HzfhKd5yzDOaAxAAnffofFQaCvK3BPrI/yDjuaFs3HsnXbWqKiplCr9uPUbdaCg9VrkXrpPCkJCbh63lTE1yJj8fJ0wVb3r7FijGRKBvyD/BAWQdy3h7GkG7FrVgnnvkFIqtL9/MmRwI8oQgiuPf4Ewmik6qq/ke6RIvlBYH/Hx3CID6P2mdMoi+h3/W8Wft2ChJPuBHQ08uSrm0tQwvLhcNt2oNHQbNvW+xon+moU4cMG4ZqSZxrK1ToT5duO6mF5gXbXurei0WujSOv/FJntlVypXZ3WmbtZaTOAGvpEehh3ciXTDYdR+3H38+CbA4fpU8mGhtUasvLTqQyQPiY824tAu3i2BTSm8/M7bsy9Z8YC2sa/xSlDNxp8vfzG9V3L99D67ONc1rem1jd/31X+M6EHqLboccg0Eb7ZAyuQaqcjunpH+s//Gq0u74DVnJrK5VatyepoJuNJC40q/YJr7a53HPf4iTmkpn6FTvsCrVuP4+yhA2ya/CU1uvflsREvI4Rg1qzfSdu2jCyHSrz+9TdU8sg77jw7biVKq5JaE/tiuJZO4q+nsW3kietTd7WGFxk5EljmFjLWrMEQGorr8OG3LP5mk5mju47z46SlDHp3HgPenkdujr4cJS08ypwU9Drb+1r8AZ56Zwf2vtlE7FSzen7ZRN2WFmnXr+OQmIiy2f3HCvgG+dHw7w2kt3qK8z5uGN56hei6B7BIkG5rS80anmQ98xQAXtG59I85SIDZzFuZf9LLtJNMXHEevQXvgEqolUr+16YlDas1BKD21T8AcLdNI01pj6LJZ7fM7RwYhNmqxMNyiTNrPgchyM5IJeDkBCSsqB/76J7y10iLx0adg6VafwIOHabKkcO0OXaCp5ZMvrH4A6T/8B6VQlIx98pLa323xR+gYYPn0ev9ycz6naysRGo3bQ42tlzes52k+Hief+kDMrctIdXGE/vMOH7+YAwxcUkYs/SorDf/9kwxeR7zTr2KnlSwuDz4r30yRcKSlU3ilCmkLl6MTaNGOPTrx/Fdx9h35DKHYrI5KTmRpbYB7FEobbCqlcybuZrXRw+659jljSY3C72jx70b3mscrQ2DP17I4vHDubwR1mtG0GvI3BKQsOxJPHcOAKe6dUtkPDtPJ1rMHQ+MB6Alz3Fq8HG8JoxEP+1vNC6gC7HH3+sSemHHUTGQzS06UStqMW7GENr5F1zXQK3Kq79sKwy84fQmP9e/1ROrXs82hP7uS3BQOD7Hvsdw9AdUKghUmzmd9Tj129xbwVm2TsFqltAN/B8qpzsfqtpk7UDhasRjvD36lmYsrXNQ3sVNWKFQULPmJ1y79iL794+lW7c5NBs4lIOLZjN9xgxqSrFc8grhp8n/Y+PGnZxfNJWDH02hmUcP7HEmq2beC5YpMReUEgrbsluWZQXwCGFOSeHa0GFcTsrldGArzno04eRHq8lS2wIO+AoLnXSZtKymo037BvhV9WPAu/OZGWnL0JR0HF3vHJZf3ujjktAajejt7+15URicXPwY8MEU/vr6HS6ui6Na3XXUqN+7RMYuS7LDwtACTjXuXv/2fqjj4cDli2kAeI54Au0To9g6cQ7uXdoT0q0leXaHIXcdI3DGWg693J3L7YP4+cWPC2xTa9EZ9v35Aa3P/sJWdQhV3a+QlBVIyPs/3VNGkZ2KznCaXHNV7LwD79jOHHMVG7tkYg45Y8pSodyiIrR3C7zGf4RHm6fu2C+oakeuXG6NUrWL69ePENi6A1OuRtFJdYjf2/Wjq40nKpWKPn26kHpwEXX0eedUltpmag7vijBbyT2bhE1NV6Q7uLiWBrICeIT4bc5Uvqn9IgZVnleBtyGdDrpsWlWzpU27+vgH3V7j9oPH6vH0pnhmzFjNBx8NK2uRC03cpn1IgLZyyW2fvXwa0OHF19j4/Rx2zPueapN6oFDcn3mprNFHRKIFnINL1qb8b1SVq+M+oBXJa/YTNWkFbifP0PWnojn1KWxsaL5wN83v0a71k9/Ak99QVFVs+PsbdCorNHnpru2M66ahNijIuG6DS9sqmHt0Iu37eSS9OJ6kbvOp8slMdO4F14Ju1uxzjh3vzLQzu/lLaSG3TnOCkq8TGe3NwnMpVMqZTY/zqxmm2kW4LgPMI9FeSsUSE032JSvWLBN2zQtfGKckkM8AHiEOKbMwqDR8Xs3KzhfqcWDKEKZ/+RxDnu9d4OIP0LJjCG0sCfyWYkNSdHwZS1x40o6cBMC5cclU/fqHWg2exKeZnqwYO3at/bBExy4LLDExGLVa1I6OpTaH8ewhck+dQG2Xf6ECKknp/DIMWVps+7x493ZX1pNywR4EuL71P/yeeI8am3Yi9a+F2HqNqz27E7FwPNYCsny6uARwxNKC32w6k6XRMT/Qje8GfY2nsPKJaiGv73kX7YWD5CgcqDx+GR59NVjMTsROvULGxnB0td3Q1bhrLGyJIyuAR4gMpR1uumSGvfgYgdX9yUjTM+7jbQwZu4n3x/6O0WgqsN8Hg5qTo9IyffamMpa48Oiv5aUD9upZ8rn/uz49F6XOzJW9D17+ICkxAZNL6S4qWat/IzssF2M6qB0VuH8xq1TnKyrmC3vQqpMxuLRHUt85s6YpPBS1lEjqFVucGnqhqZdXdF7j6EHNiX/h9dskhKeWnC+Xcn5QSzKvHr+lf9SxFdRS3czSWs/FE6vVSq9KblzwUGLKUfK3ZTFnA+eiVKnQtmqLez8dYEVSmHDuW7VUnv9uyArgEUJCYLRoMFms5OrNPDNxJ3+Y9IRjYRnOvDd+WoH96jWtTQtTIhvTKq7FUGTGY1CrsAv0KfGxXd2r4FE3nYwIB47svneEcUVCnZKK1eP+D8bvhvPrE9D55Pm2WwwWFE53zAhTLpjXTsRqAW3/sXdtZ1g1heRQe4RVwm3M+Nvuu4X0pvaqQ2jf7I50JYPIJ54hfN44rFYrxuw0riR+gXNWJVxPJKO8ksHVxEw+2xzKHLURY/D/OJD2KiBx/chVMq/lJejTGvZRSfMKXn3TUDmXXtnPOyErgEeIp9v0INPkwHNT1/Hc59s5i4XPsWHP+K7YmbO5lH7nmJAGnjritE7os3LKUOLCo8xJx6Czu3fDYtJp8M8oVFbO71xSanOUNEaDAZusLJQ+pWtXNmz4GX2MHpUdVBp598PessZ0/Srq1APkZPmgrXPnNOBCCBRX15J6xQ6nxt5oGxdc0lGh0lB15A9UXrEQEWhH7jcrOT+8HadmDyNHmcP0I6+Qk6BHfTWTp7aF8ovGyBOZEt93rYVn8yH0drrOYy6fw/w+6LdNg51fo2rYAVWz/qX0G7g7sgJ4hOjTuCW1densi1dwwmriPVsHhn7VEaVOjYK7Vy6y5gcMVtT6RtrcbCx2pWfq8K7cCLtKOWRE2mMxF2wqq2ikXLmC0mpFW9n/3o3vg9zVeVlgXPu2xbbrbVnhyw1LSiLmKZ1QKC2oHv/srm2NhzeReiHPPOT+0b3rZTsGheA/6mcAlEeT0c2MIu1YfzJELhMa5/2+VZHZNA/L5ceedVEqFVR/tjbZLrVINX2JDVno9vwP4VkbHvsRyiniXFYAjxCSJPH3uCf54+lqHPm4GyM/aYekkEhPTSVXoUNnNRTYTwjBiZgsfA1p2NpXvLTJ6Wcuo7FYUDqVbu4et2BbjJkajuycXqrzlBTpl/LMDLZVSzewyG70PJQaCwmL93Ct/xNYEqNLdb7CYM3NQf9VO2wc0zDWfxdd27tnmjEufJ2MSFtc2gSgqX13X6Tc82GEDx1F9GvPYbVRkFbLnd1tJ5Ea3p0eKZ4si8g7B6hik8mPV8zs/O1PrFYrSpWCaoODMYlqJBm/5XTOUzBiI6hLt+zm3ZAVwCOGRqOhVYNgnGxvHob9+ec6zAo1zasVnG72wo6DHLX1oZd/xVv8AeK37gfAptrtJRZLkubd/oekEFw6tKxU5ykpssOuAeBcijEAANrm3am2/wi2/josBglLYlSpzncvhNFI9qftsbOPIddvCNonP7lre8PpPaScBqXWgvuX8+7aNmHGRsIHDiD3+HZsW/fBf+lyLtZ+DgCNnRWtVUnTa564e1xj47gnuFI1lpqXfTgyZQ2Jc8+S+MtpJDsV17RB1Ph4GpKufGNrZAUgw5ajeW+KQwb2KvD+L2tOoBJWRjzTqSzFKjSZp/LeuFxalG6pR7/AVjj4ZZF21RGjIbtU5yoJjNevIwDHatVKfS5z2FlyIvU4NfG+5xt0aSIsFjI/6YGD7SVyXbpg8+KMe/Yx/PwMOQla3Lo3QOl+dyeC7H27wWJE6eJDpY/fxqFGbfo83x2FZMGYnbecXlFbyMx0IjojFfs+/vztsh3fRFdyI1Nx7BqA95imtBzfEp1D8Yq7lCSyAnhESE7IZsjoTXR/ZyMvvrWZtJS88POUhHjO2dfCWx9LgP/tH/6os5dZr/Kht30O3p4VMxLYGB2BFfDq1vqebe8Xr9p+mLLVbF9V8esFWGJjMdjbodKVvneJyEwBwK5N+1Kf625kfPEUjrpj6G1DsHlz+T3b60/vIfm0GpWNBZdPZt92/7/+/t6f5qWFtiRHcuWbbwFQ6LQkuh4nVWFhj85EvR5qDHpXukxdzYubX2Nt0AFOdIvD8/0QHDv7o9BVHG86WQE8Alitgme/3sN+nZlolYWttibaTdzOjJXnGfP9H2SqHentVXDCt18X78KiUPLGkJL3ry8p9rvmsrFBEBqnexfdvl86PTEFlc7M1R3XSUm6Wurz3Q/KpCRMLq5lMpc5KQ4AhXPZBjL9G+PhNThatqCnKrp3NxbqYPXsl2PRp2gw5ypJN9xar/fE3oPsa9ueVV/czOuvq+4PU/IOlM+Gb2LPoW1Mnf0TVnUuzvXSOaYzMGNXLgDC6M6Q6i+xsv9KHus0EJ1t+dn674SsAEqJnJwcDIaCD1XLmtGjtnJWa6GD0oaz3/ZmbA1fdFb45tA1tilq4GNJYeyY2+vFpsQksMLgQidFCtWq+ZaD5BUPe0dPavUJRp+iY/VPwytEAfU7oUlNBc+SKd14LxTOebEGIrfsTWPmyAtk/zAMVryM1SqheuVvUN454Osfkk5dYVXtm0nnItu3Z/XTw1j1xTesGv4CmpdG4J6cRI1F81k3Pc/T6cyulWR/+CnJDjC3q4JR58aQrk6nnl9NXhrxGANcY1BjobaHhp3vdWVc65HYqCrewv8PFWcv8hARExPDwoULcXR05KWXXkJVTrn2M85fZPK3v7POqzneZolfP8/bnr86oiEvWepzKDwFBIQEuqIqoPDE7PmbyVG58MaAmmUteqExG410Px3GNb+yq97VbeCPxF1qSeJpN/6eN4ABI/4us7kLS25GBrqcHHJ9Sj4wriAscdcBUHmVbS6b7LUL0OwejZ2tFQP2GILfxNY7oFB9v1gTxZJ2g7FtOITePibC/lqF84F9VDpxlDQHJy4PHEzrka+x/733qTH9RzZdOILH7v3k2qsInD+fX7SOPLX1GfZW2stTrZ9i3rx52OXGM71rTTq3b4tKVfFSYvwXWQGUMFFRUSxcuBCVSkV8fDw7duyga9e75xMvFTmuXmfotN2EV2pOy/hLfP3Bk2jVNz+QSqWCVkF3z5y5KklBE2scDZtV3CyYl/fsRikEJo+yedP9h4HvbGLBB/0J22zDkpzuDH6jYqXJSLl4EQnQBRRuMbxf1FVrAaA/fhDb3s+VyZwXdvyN76H3sSgU5AaOxfG5DwrdN/b4RWx99lE5tR3v9WuNWqmgftM8JwKDXo9ao7lRLa3rrz8T0bgx/lv3Y1ZC9cVLqRSQV5ZyeofpvLD3Bd7c9yY9U3oybMgwqlcvXW+0kkQ2AZUwe/fuRalU8vLLL9O4cWP27dtHREREmcvxzoarxNu6MjH7CH/MGUVgzYKTvd0Jo9lKtMaJhvYVNfQrj4g9uwFw+E+d2NLGxsaRQR/PxL5yDtF71Dz11UT0d8ilVB5kXMnLjWQfVDb5ZbRNu6D1UJK6fg/CUPoFhM7u+hP/nS+TLDnxsu0bHCyiQ83p3xfyVeS3jIuei/o/6Ze1Ot0tpTIHrv2T5PzyAcplc28s/gAZqgwAFCoFgwYPeqAWf5AVQIljtVpxdHTEycmJ7t274+LiwsqVK9Hr7++PIiEhgbS0NACOHz/OggULWL9+PZcvXyYiIoJLly5x5swZDh48yLT1izgclU3/9m4MnjYeqRhRhglxyQBUci299AolQc6FCwAEtCt77xNXj2oM+3w1J+o0p9mpvbz56cfEpKaVuRwFkXMtPwagFNNA/xf35wZjTLWS9mPh38SLw7G1Mwne/jIxKl+ONRvH4JbLUHrN5MySwVgt5nv2jz9yjla2eSk9eqWsITLuzof5b8/6nWPuwbz+ZjWeGqvkgObmud7C8wt5e+fb1Hevz8onVlK3askU3SlLZBNQCaNQKG64jmm1WgYMGMC8efPYtGkT/fr1K9aYVquV2bNnYzQa8fX1JTY2FkdHR65du8bhw4dva3/G7jrwOMvPHEKXeJ2mvv60aNkY17tUQfovseF50ZyenuXn1VEYNImJmBUS1VuVvgtoQdjqHPjtfx/Rd+oMOhzYyPeffsigt8bQolrZZ3b8N6aoKCwKBQ5lZAICcHh+HJp5S0heup7LXboRVL0Vbg4l+/k5tPgLml/8jrPaBognJ6MMe4lcswNOcU4k+B4he2k7GvdZicbR645jXF48izaOiVwK+piAsIlEbfoc/2fn39Zu/7GTLA6qDZLEd/U/4YMLnzLlwBicNTMQhnC+O/IdbX3bMrnDZHSqsk/kVhLICqCE+bcCAPD396dNmzbs2bOHGjVqUKtWrSKPmZ2djdFoJCgoiJycHBQKBUOHDsVgMHDx4kUCAgLQarVotVrs7OxIzkym07xfMaW1ZF4mzLuchbRjJ7WyLrLok8G4evndc874qAQAvHzL1rZeVOyzs8nWacvtoB3y/s/Xjh7JG26uVN6whMPfvIZ++EA6tL177vnSxBoXj8HREcV91kcuConxiWTmqnC2yaHZ6iFYkbhiX5UEr0YoA1rTtM2zxS6oI6xWDs95m+bR8zlu1xbvZ2ew+8jT2KpMBNdeQLBfXS6teJNoj40c3NyRhs1+w9H/throJJ48SxP7P7luaECNYe9xcPE1ml/8g9T0BFycbn7WI69H82J0Os5CkG1jx4dhGXwS/DHjQ//H+J3PA9DSu+UDvfiDrABKHIVCcZtrYPv27bl8+TJr1qzB19cXxyIW58jIyLMzNm3alJo1a2K1Wm/YKH19b3fPtLW15cy7/+PF1SPR7TeiTnEhWufNGce6fDd1Cl9/OemecybEpwL2eJdCeuWSxD5XT6pD6fv/F4bpw57mB3cJsWQ+J2f9SXrUBvo9vaJcZFEmJWF2LZsYgH/YP+5/VDFYif9xHgb7TNLCD2ATc5y6ERtxvPon+7MTadWz6OYhi9nEsZ+fp3nKGg66PEa9l37hr+1P46lLwN57OrX86wNQc+BPOOz4hUva7zl2ejC1EsZTKWToLWOdWvEFXZSZ5DbIc3u2q90H5cVFhIcdwaVRnrNDVlY2z+47Ra6TK3/7O5Odq+cFg5r3YwVBHmMJT/6VGo5+/NT5O9SFcDetyMhnACWMJEm3RQ+qVCoef/xxTCYTCxYsuLGgF5Z/bP//KA5FIWz6OrWORU/MYfakhcyYN5UF4x7HVbKQKAqXqz0hM+/MwsunZGrslgbZKSnYGkxk2VcMBQAwuudgOr35OCobC1dX5/LiqLvnoSkNhBDo0tKQvO5sBilpju89QPCBvYQPfobmbdoT3LAPzft/Sf3XN2A/LpKzbk0IOjkLvSG3SOPqc7M5PaUfzVLWsN/3eZqOXMDyHW/iY3sek/2HtK7T7Zb2vh1fpVG1OSizVZxL/ZTLG24qHCEElaVQUoQTNQY/DYB/laYYJRWGU0sRVitWi4VXFvzJBY9KTNGaaFCnJq1CGrK+XiCVkhI4pfKgQeAElnef/MAv/iArgBLnvyagf/D09GTo0KFkZmYyf/78QisBIQSHDx/G1tYWd/fiL8YfzzxPMmp6U7hDwaQcC3ZmPTptxf2Qh27eiARIlYvm4VTa1At5mUavfYbZ1pZacceZ+M6TmM33PpwsKbITE9EYjagK2B2WNBaLhVVffIMY+TqpTs50fPP2gEKFQolo+y5ehkSO751T6LEz05IJm9KdRtn7OBj8Aa1e+oElO8dT2WY3iQyjT4vnC+znXKs9zTpvwTbahUjtn5xY1g+L2YDZbMbDFE969s1lz8nRnaM1h9IifBUXprZhyc/DOFijOiPPH6d/p5tBYs4H9jH9u0/4evdGFjSujq4MC7eXJg/HU1QgCjIB/UNAQABDhw4lKyuL+fPnk5Nz7+IqoaGhRERE0LFjRzQaTZHlsVgsTFuwnC0mBT5SBr3e61mofslGgaulaG9rZU3sobwSjW4NG5avIAXgoVajyNIjJIkTjvE0+r0RCRkJZTJ3Sr5nlE1gYKnPtf7Hn6mxaD5RjZrgvWgRjk4Fmzfr1u/JRec6BBz9CVMh6ikkxUWSMK0L1Q3nOdLkO1o8PY5V+2dSSfEHUYZODOpw952Vzr0yzZ/eg1tEbVLcz3JoRRvMmbEkGhzx0mXd0rblwCkcbPIOtdLOMSR5Hfv3D2FM+5svSumrVxM34TNcW7Xk2Q/fwaYYf4cVlXsqAEmSdJIkHZYk6ZQkSeckSZqQf72BJEkHJEk6I0nSGkmSbvuflySpsiRJOyRJCs3vO+pf98ZLkhQtSdLJ/K+CU1E+YOTm5iJJ0h3v+/v789RTT5GSksKl/Hztd0Kv17N582Y8PT1p3Lhxoea/lp5I05+W0uDDP6n34Qrqf7SKSaG2WJAYUMsOrWvhUjonWxS4UXH82gvCGBYGQLWu3e7Rsmy5evEkaz4bjwS41Q/mfJU0ADqv7Myh8NKvK5x5Jc+t0aEMsoAqd+3kemBVHps7kyrVg+7YTlIoyGn9Dr65MRzfv+CuY14Pu4j+1254m6M533EWTR97md1nNmCT8x0xuXUZ3GV6ocygCq0NDZ5bTUDy4+Q6p3Dytw+IzHmJWPOtu2BJoaDFY59yccgGLvs9hrspHdWSvkSt/JaMjRuJGfshts2b4zd1KoqHaPGHwu0ADEAnIUQDoCHQQ5KkFsBsYKwQoh6wEhhTQF8z8K4QohbQAhgpSdK/I3amCCEa5n+tv58HqQiEhYVx4cKFe3r6BAYGolAoSExMvGMbi8XC8uXLycjIoFevXigL6c3x54aXSLxuj50qBw8pm0qKTJrbJLD2tRDGDO9b6GdJRoOrsmIHgdmmp2NQKfGuUXa+7vfi2pUz/P3J/wBwrlud58d9z5lnzxBoGwjAi7teZM6BwptBikNuRDgALjVL9/ey4+81BFwKRd+6TaEW5IZNHueKQ3W8Dk3Fcgd//UvnjqP+rSeOIoOovkto0OEJTl87Tkbse6QYvOnTfh5adeGjviRJotrA76jvOYXKca9Ty7kF560FuwwH12iFqu0gopq9jlJjxWXzd0SPfhubhg2p/PNPKLTln765pLnn/5rI4589kzr/SwDBwO7861uA22rBCSFihRDH87/PBEKBhzKrmF6v5++//8bNzY1One6eN1+pVOLm5kZSUlKB93Nzc1m1ahVXr16ld+/eBBZhKx+dlZeLZfO4J9j+1XC2ffUsSz99npoBRfPmSVXa4K69807m3+QkpnNl1X4MKUU73L5fHLJzyLKpOC54p8+eZMn/PgbAqWYVXvh4yo17awauoU/lPvgaPHHfYmXZV+tKTQ5zVDQmtRrbUjwEtlqtqL7/nujKgXR7561C9ZEUClJajiYwO4Ljh5bedv/ssb24LeuHBjPpT62kRpNORCdd5/KFlzFZtbRqNh8X++J5Nnk0ynv5Oa+MIrZNYIFtrlxcTZXFwwg4Ph0LsNziRHhddyrP/BWFXcUOiCwuhXIDlSRJCRwDqgE/CSEOSZJ0FugLrAIGAnc9iZMkKRBoBPx7D/yGJEnDgaPk7RRSC+j3MvAy5JlPKiobNmwgMzOTF154oVC2end3dyIjI9m8eTNGo/GWr5iYGPR6Pe3bt6dJk6IVOUlU5SVFi0lPpoZN8T60ZrOFHJUWR5uCD4CzopO4tuUUMecSSEhVkKF0A0lBlZ2r6TVlaIF9SgP7XD2x7mXr6ngnjoVFsvC7n/EVFtSuTrw4Ydptbca3/pRDu/+mqt4XqxBs/mQX3T4r+QhmER+PwcnprqbI++F6eCQ7l64gJCmBKx98hK4Ii2OT5oMJ3/ctrgcmI1o8fSNKPSb8IpXXDEKvsEF6dhWVA+tiNJvZeuA1PHTZeFedh7/H/QXXZbzuyv652+A4ZHVMx97hZn2LzIxobFe8iBU4ZWPLfAdbrjWszdzuc1HaVBwvs5KmUIfAQgiLEKIh4Ac0kySpLjCCPJPOMcABMN6pvyRJ9sAKYLQQ4p/XxBlAEHlmpVigQOd0IcRMIUSIECLEw8OjUA9V1oSGhnLq1Cnatm2Ln9+9g6wAqlSpQnZ2NocPH+b8+fNERkaSkpJyI+Dr1VdfpWPHjnfsn5qVQ/Vx66g7dgW/rVh3w/PogKYDAAvDi5+WNyclDQA7Gy3nfttG2OoDZEXlmauO/7SOhROOsfugRFiaM2rJRG2PJLyIISLLHX1yerHnLQpxly+hNVvIdSr/IjVX4uJY8NnX+OljuFKjHW/N+L3Adnt+W0VVvS/hLfUkmwW1jQq2jd6JqYRzCKmSk7Hch8fY3Zi4bA1pvXoRMm8m4VWr03HQ40Xqr1SqiGv2FkGZVzh57C8AjAY9WYuGIiGwDFuDZ2BeSoU/d32Hn10oCqd3aBTU4r5lN0RupDkneJE/4Ptgrp0/cuPe5T/642k0cLjnBEYFVOeMewC/dvkVd5uK6wZdEhQpEEwIkSZJ0k6ghxDie6AbgCRJNYACU0ZKkqQmb/H/XQjx17/Giv9Xm1nA2iJLXwHIyspizZo1eHt7065d4YumNGvWjJCQkELZTv9Ltt5Ioy92AGBCx6dHBHOPLSLIJhdbixeZKg2v1y1+IfDstEwALOk57LzsDuTC+jMoLXosShvcRCwt+lbDt2Mr1PkmmKtrDrFxXTaX/z5IvRe6F3vuwnJ58ybcAXXV8k25EBabwA8ffk6V3OvsrtSBVZ+/V2C7w7t3UvOqDxeCYujS7yn0nQ0c+/ggwTolx8bup9aHjXHyKHyqjjthtVrRZWSQ06D+fY/1XzKTUgme9jMC2Dx+EiOf7FasCOwmrZ8l6uAkbPdNRjR5nONz3qSF+RInWk6lUdW8I8JL0ZdxFfOJNjRjWMf7j6i2Wq38uCsFL1N1rA429NZv4OyJ9VSp3ZQTW8fROO4Se6q35duYbZitZmZ3n423fdmmti4PCuMF5CFJknP+9zZAF+CCJEme+dcUwP+AXwroKwFzgFAhxOT/3Pv3b3cAcLaYz1BuCCFYs2YNBoOBAQMGFPmPoTiLP0DPyXmLv0YJM/oHUE+TSIzViW3ZfmTrVfR0ScPbvvi28ZysvN2DNv/gOdg5joaVU/C1SaGhfypPTnuSwF7Nbyz+AIHdm6A2Z3PhcCKG1Mxiz11Ykk+eBMC7efnVn02PS+L7cZ9TJTeCqzXbs+rHghf/uNgoHDbpibJLpO3wvHxQOnstLSe1JdRowU+jIOKb40Scuv9i6hmRkagsFjR+JRsbcWHXYU726ku1pEimNniSWdd1hXZM+C9qlZqIkJEEp51j+y8v0SJhGQc9BtKo+7M32uw9+R0AnZp9WSxTVkpiDObMvM9hdkoMH3/8KjszW5GdI6htCgXAvW4X4qIPU33/DC7auzDf1Y3orGh+6PgDNVxqFOvZHjQKs2J5AwvyzwEUwDIhxFpJkkZJkjQyv81fwDwASZJ8gNlCiF5Aa2AYcEaSpJP5bcfle/x8K0lSQ/IOlMOBV0rmkcoGs9nM6tWruXjxIt26dcOzjCovAURm5HlQnP60OzqNip4t6pKVa+DI+asE+XriX+n+tq2OlTxQiDAuJhqoBQhnD1qPvXtNA6VGRbBfDmfjfJj94REkqxmV1YhSGPF1zqXrxKdQqksu84jIL3heq1uPEhuzKGSlp7N+3ESCcq5h9G7PzxMKcoLL+5xcWrAfH6srXs/URau9qTQVCgVdJ3dgx8T9VE0V6BeFcSI8nUb96hRbrtSLFwGwrRJY7DH+IT4+ldDdR8g8coTKa5eCzo6Mb6aDyoOs3RH8diaaZ+sXzuT5X5q0eYH4wz/QOeFPTlqr0ujFm2cmF6PO46fdQaK1N37uRdvhCauVwJ1HMUga6oavwfF6ChEWP5It/WgqneKjN1/B+7e82r9RJzfjHj8be2BRzXYcTjjOl22+pJl3s2I904PIPf8ihRCnyTu8/e/1H4EfC7geA/TK/34vUKD6FkIMK6qwFQWr1crUqVPJyMigcePGtGhx//bJotARFbsxc/poFM1aBQJgb6OlY5OSyYnv4eNJK9LZaOeNd6YVz3qFe5tsP/4pfFfuJ+Z0FCa9BaPBQk4OXM32wfr+EnpMGlLsXc9/sc/KIkerxq6M890AxGdlMu67b6mdfYGage3o/U3Biz/A7uWrqZHmzbW2mbStWvBbZcexrTjy+ylcTqXjvD+ZXZEHaf9m8T5TWVevouL+YwBy0jO53qUTXiY9XkCEdxAN5/5KpSq+1Mg1svXQdX7cfqXYCkCnteHP6iNpc2UmmxtNpqE2r2xiYlo0x0+/iZNKRceQgndUd+Pnn5/GUOdDAJRZZuJNHlQV13nLJYZhY8aiUCg4GvIdIUfH0OHaDwB85VuFv5NPMLz2cPoGFd5V+mFAqsg1Tf9LSEiIOHr0aImPe+bMGbKzswkJCSmUGWf//v1s3ryZGjVqMGTIkBKX514c3XCW13ZFkoKgj0LD/0a1xMOrZD0VLp2JpM+iMwSq1Gz8vMt9LdzbPlzMhVQvgp3j6DKxZH5fBxs3Qq9R0+Hg7emwSxODQc//xo6hUsw1djfryqp3R92x7blTx7BfnMkV7zg6vjnonr/DK/sjMK6MwF4BF5SCrl+2K7L548g772K/fj1VDh5A5+xcpL7/xmowcLx5G+z0Wbis30ylqre+BAxccZwjR2KZ/UoLulQpXH6p/5KmN1Fn+ykcJAWhPRoRGnmMi6GvolFko3L7gu5NbvMsL5AlK//glxOZ1He6wKaUtlS1iWLVmJfQaO9cizcpJoLomX05aw5iglMQnRpmMr3zdFSKhzM/piRJx4QQt6VHfeRTQaSkpLBixQo2btzIF198wfjx4/n555/ZtWsXu3fvJiEhAavVitFoJDw8nJkzZ7J582bs7OwYNGhQucgc0rMuv7bxpT9q1lqN9Juym8TYkvW+qVHPn2dr+nDJYmLqkuIfz5iz9dg4aZGEhSvJLphz7r9alNlsxk5vIKuMfbMtFjNzP+1PpZhrJPkG3XXxT09PxbgihhRNJk2f71EoBVqtVQBeb9Yn0SyobVWw8+1dGPWF9xC6tHw59uvXk+PoeF+LP4Ck0RDjnx9IVoCt/6tONRFKiS+2Xyz2HM46Nd11tqTpFLyy7Veirw7GKsDVf26hF3+zxcrYQ06EG/1YndgFL1Uyc4Z1u+viD+DuE4DLqD18ZH4BVW4jvmv/3UO7+N+NR++J/8Pp06dvu5aQkEBCQl7elu3btxfY7/nnny+zHPQx4WGsW7EaLx9vejzeH41WS+M+Dajb2UDzqft4PzWXxT9u4q2JJauQxg5rwMZPE/n9TAyjKZpXidVq5cy8bRzbn0mu2hkPEUPrYfVR2d5/4Na5zQfY2246Ttnb7nuswpJlMPDX9x3IueaGXbCVdyb8cMe2VquVo/M3Uc1YiczBDjg6Ohd6Hjd/Zxw+b8Whj/ZRXafk1Lj9VBvTEBfvu7u7xh44iGH8BPRublSeOrXQ892Jo6u3U/3SMaKadaRWAUGE1V1sqVbdlSsXkzmflEVt9+LtQL9qFsT6I6GsVrYgULSgm48Xge65GI0paDT3Nu9tP59X+tJJnc6JTwaiUKmgELsmIQQT119GrZRY9kJvHDT374H1IPLIKoB58+YRERGRFyperRpDhgwhNzeX5ORksrKy8PX1JTs7mwsXLnDgwAGsVitdunShQYMG2NoWLp9OSbB1+VJ+3BHOGce6kCCotXcab7TxpffTT6Ox0TLw/Y588eFykpT7gJJVAEqVgl41PPk1NJoT5xNpVLtwcRjXNhxh/4qrpKk8ccBE144Kqg96psQCk+JOHgIaY1O57OqvTpryBvan3XCvk8LQj/bc9Vn2rt9IcKwvFxsk0rlhhyLPpbFV03pSO7aN3U1NtYLoSSdJfaoKVZsWHAiZm5RE7Ki3QKul2sLfcCkB19jolasJBhp9/r87thnfpQZDLxzgo+0XWTmoaAGLQghWJaTx8/W8F60misu0UMaSkXKYU8l/A2BjE4iTUyOcnBrj5NgIe/sa5Pmi3MTJJu//wcs+FYW68Jlr/zoezfozcbzfI5i6vuUfS1JePHIKYO/evaSmpt6o0dugQQO6d++OQqHAzs4Ou3+ZFZycnPDx8aFTp06YzeYyrzq16NeZTA9VEudYl5aqWEzZmRyzD2bC4STOHnmH1z/9hP3zx5FBbzyktFKR4fF2AfwaGs3KfZH3VACJJ6+wZ+ZBYq0+aIWO5rWyaPTa4yg1JZdSevv2Xfj/Ngdv1R/Y/lJ2FbciLTWpTSye/X64q/tj2JWLVN3rQJhLDO0HFs6MURAKhYKu33Zg16QDBCSYMC0P51h4Ok0G1rut7akvvsQpIxPVpO9LZPFPioyl2qGtXGrcocC3/39o6+eKp58DJ84mEN/HgJdt4XPlTI9M4MuwWAJ0Gn6tE0A/z4bAQCyWXDIyzpCecYL09OMkJ+8mLm4lAEqlHY6O9XFyzFcKTg1pHBiAp+1eknIdsVisKAuRpvl6Sg6frj5Hs0BXXml35wR2jwKPlALQ6/Vs3br1xs9BQUH079+/UH3LevGfOnESvyb5YdRoGdtIw6tP5S1281Zs5atDLsyiHUs/X0uKqjdVpRieeeOLUpEjuIoL3goVeyOS79gmKyqRfT9u5mq6BwrhRh3vRFqO7onWtWiVz+5GdnYO818bSfsjh7ACVrWZqq1eLrHx70UnkxdxwKWvvybgwzHUbtCqwHbXDp8lGE+o41Ain5n277bk+J9ncTicgtvRVHZE7qfjuzfnzklLQ7dzB5nBwTTrXWAsZpE5Pm8plYWVoJH39sx+u0MQ4xad5H+7LjOrZ+GKoustVqZGxNPNzZH59aqg+NduSqm0wcWlGS4uea6YQgj0+uukp5/I+8o4TkTkrwhhAcDWtgp9qrjxW0QTXvtzJdOe6ItWdecXDotV8O6yUwBMGtQApaJ00mU8KDwyCuDAgQNs2rTplmuFTdtQ1szcNo8fM3zQSjBrQAAdWt70wn3+iS40Dr7E+HmbyTJa6KM7x1sjX8W1UukVRWnp68zKyCRi4rPx8bq5QzJm5XLkx3WcvabDrPQi0DaONm92xCmoZPP9rVj4B94/TKJTdg6XfLzx+m4yLZo0LNE57sUz44fy9cgDkBTBhq++InxAH3oNfvW2dvY+LnAaXEqwlnLjJ+tyLTCKrCVhVEsws/n9nXT9pj2SJHF+xgzs9AZcXyk5ZZi7eSMRLn50b9Xgnm2fruPD586h7Dgeg7V77UIddm9PySDTYmWEn/sti39BSJKEjY0/Njb+VKqUF0RnseTk7RLST5CecYJU7TFsKp/ngB5CFn6JnRRIVYdaNPdpRN/gNlRxrXRjvJm7wzgcnsL3AxtQuZCp0R9mHhkFkJl5Mzq1TZs2WCyWQufYL0siYxL5Zl8WFuHCu031tyz+/9Cgbg1WTiq7SMW+zf3463oSK3deY+RTdbGaLZyes4Vjh3PQq13xUMbSZngVfFp3KdF5I65Hs/v11wm5fIlsrZpNw57lrQ/fL7FYgqLy4U8/sWLWd4Rv3UXoyrVc2bKNN2cvu+U8IPN6ChY8qFrCKaqrhPiR5uvA5e9PUlutZM87uwgZHwIr/ybLy4uaPQtX6OdeWK1WgpIjOehdp1BnNpIk0b2xL39vD+Ovy4k8GXzvDKTL41JxVStp41y8g1el0hYXl+a4uORFgdfaVpmzDg60rvohx+NPEZVzkdMZ6ziTtZpZoTacGH4AtUrJ2eh0Jm2+SM+6lXii8UOZlLjIPDIKoEWLFuzfvx9bW1tcXV1p2LBhuS0kd2Puq+Ox1O6FjfdiXhi0qLzFAaBtI2/slp9ix6VEeq4+wP7VEaSrPHEkjXZd1VR/4pkSnU8IwcKJ31H3j4U0Npk5FBxMq+nTGV25/HdsT7w0hqO16rJr2k+YsnKxmM2o/nX4qIo3k2CTQoBdyXuVOHs70eCrVuz/YB/VdEqiv1yPDaB8/vkSO2A/tWkvOgTq5oUPRHuneQArd4Qx92jEPRXA+axcNiSlMzrAC1UxzS9Gi5FZx6dhuLgOr8x4Xk7PYEhGJvYvDiMv8QBcTU6g/9rOIBREpidS2cmT95afwsVOw8TH65daptQHjUdGAfzj1pmTk8Pq1avRaDTUrVs4m2VZctK9GkFpUWz9emF5i3IDpVJBI3sNR7JyWLveiq3Q0rJeLg1fGYhCVbx8MP9FCMGVyItM3ryIvZpV4AMztFq2vfUOY14quPZruXE0L5VAg8CcWxZ/q9WKe5oj8V6lVxdBo1XT4YcO7B23En9RCWP779CrSq7U5PU58/FR63j8g5cK3cffyRZ3b3tCr6RgsFhu5JAqiEnhcTgoFbxSufiZfZssuulxJNlIXHB3ZVSXH/m3I2qQmyfNnIZyKG0x/Vf1wy7rCeLiajN7eFOcbCtuneuy5pFQACaTicWLF99yrSK+/QPYmg1E23tgMpnRlKD3zP3S21/B3lBQKi4xbPIwtE4lE3k85+fXUV2z50KIDvc9SeTUvWmvDd6+nTaOJXeQXBIIq5WrpxKwV+lo88mft9yLj4vG2exAkl/pF4Bv89UAdo2Zg7+iGuaTdpyK/ZkGY16/rzFjLkdS5dwhwtr1IcSlaL/3vg28mbfhMnPPxfBa/YLPo85l5bIuMZ13Ar1wKYG8UE9ofXgmoBeBjV9Arb398zin/wdsvdKDsbvHke20kHbu79OldukVyXkQqZirYAmjUqnw9r6ZfLRbt273LNtYXnRKv0yirQu935zD0U17ylucG/R+rAkqq5l4b4cSW/xXLPuV2glrue4CNil61gfWp/Ux+Fi8yJlhJ/GoYIs/wOXJ3YjKcaJ+pRR0drcGKkVcyKvx7BlUegfy/6b9dy+QWOUaSrMBdUwQG0ZMv6/xjk6bjUIIGr5ZdPfax+vmuYueCL+tptMNfgiPx0Gp4GW/kqnrMX7wJqq3HFXg4v8PHarWoYqHLQ5qF77r26dE5n2YeCQUQEREBFFRN1PttmjRosLaAJ9b9jNjz/9FvK0rQ7Ym812PEZj0hvIWC0dXJ2oZkzmUWnJ1gqWTP3Lcrz/2sadoEtiKrzvX5eVZnzDouVGgKBnTUklzPjQLndJEnff+uO1eVkQyJslMUHAtTKZ0rNbS/39r9upzuPTzxFFrSz3PBnz+xggsFkuRx8nJysFr13rCajQioG7RA+xisvOe1cu+4FiAS9l61iamMcLPA+cSzAp7LxacW8CFlAt81voTvOxdymzeB4VHQgFER0ff8nNoaGg5SXJvVFotr66ew6IODtRLuspPDZ/gsfd/49qFC+UtGs1dlVxSu5BaArV/rWYry32exnQtDvuer9C7Z3+aNWpTAlKWHkcWfs7VLHcauMbh6H27l48mHuLsktFqdXzzw1AaLAyh6bwmnLx4qIDRSo607bk3vrdNTOD1DzqRnl5wvek7se/XP3A0ZOP13LP3blwAFxLyyoZX9yj4bXxaZDw6hYKXSujtvzCEp4fz88mf6RrQlS4BJeuh9rDwSCiAZs2a0bhx4xs5+3fu3Fm+Av0LIax8s2YUteevYfjS7ZjNefbj+v16snTmKNqnXuWCrQ+fLTxYzpJCu0aBCEnBtnX7i9XfYr75Zrpw7Oc86bkDn6pWQo/MLyEJS5d9m7MAFcFvz73tntVqxTPTiWx3M2kxV3E/lI0kBHqFkWEHX6TegnoMXfL0jdKdJUXcxrOoTXk1qN3Hh3CqWho1rjvw2YdPceli4TKlWq1WpJXLiHH1IaR/8RbKy4l5CqBOpdu9nyJyDfwVn8pwHzfcNWXz9m8VVj7d/ylalZZxzceVyZwPIo+EAlCr1fTt25fXX3+dVq1akZKSciMVRHlisZiZsHAYv+zrQs4FBbtP5PLYp79x6HBerVKVWsXMaa+gVhhJ0uSUs7TQonsr3AyZLDsVV6j2RrOFbZsP88EnC+g0agHVPlpP87cX8/6ncwnzTWd5Qh9srp3ngxcn33uwcmbmDy9iMZxH0gbhEXR73pvo6+HYW2zR+DoQ1Wsg7c/GslD9HBOqjL3R5pThLIN+GFgsE01BmHOMmHfm2dxtn/BFp7Phty/3EtVYiVeKjoXffMymrfPuOc6JdTvxTbqOud/AYjtH7L+WDBoFDdxu3wFMj0xAicRr/iVbNMkq7qxMl19czvGE47zf9P2Hvq7v/fBIKIB/ExQUhMViYeLEifzwww/89ddf5aYM5q/7gvnnh1DL7RK/v1KP+rpkQi1evLXyKov/zCuRPGjqj5isGnyqRZSLjP9Go1HzpIuewyoPDm69u1lj5/LNdHpvMS9sT2RlrhOuSivD7dIIJIc/9R4siO/MqSg/1ita4ul7f8VLygKPVaGAlRZtC17Erl++CoDTnoMo9XkmmQbPvMfj7Z7hzLNn2NJrA9WSKzMm6gyZn9XAWgJKIOazAwAYHU24Nr2ZA2jKB6uw6VkLnVHB0QXLmDbvnbuPM2c+mRpb2rw6tFhy7E1IJzkqk8bB7qj+o0Aicg0sjU1hsLcrlbQl69XWf2Z/3l70NnMXTcZsupk2OzYrlsnHJtPSuyX9gvqV6JwPG49kQZgrV65w/fp14uPjuXDhAj179qR5MWvLCiEwGAxYrVaio6PzinLrdJjNZrKysoiLi0OtVmMwGDCZTGRlZZGdnU3t2rV5Zk0aAJKthNVGQ10fJ2KuJpCaAyosVDFkEqGyw1GZyOJRHanuXf4LZXJCCr2+3ohFUrB6VHt8AvK8q4QQREfEcexIKJsOXma92o9KxgzerWVDz8fbY+928wBuw8o1vHYob6F4234Bo/63rFyepbCkhF9i4XujcM/M5ZlNOwtss3XeMoJOqNFvzjM3KNt1osbMn25pYzKZUX7hgULKf3MdX3ANh31jniLZMQ3LGV/UFoFWo0Fna4uNixO2nh7YBfhxOkxPnXRPNKjwm9i2wHE27viNgwt+xylbRT2joMuq9be1uX4hjPT+fQjr1J++P39VyN/Irby04SxbdkWw+PWWtPS/6RmVaDTR7/gVEo0mtjerSWWdpljj/5cvt3/JqrBVIIHClMO8H/KUaa0LoQgheH3b6xyLP8bKfivxtZcjfuHOBWEeiTiA/1KtWjWqVauG0Wjkq6++YvPmzVgsFmrUqIG7e+G2iykpKSxbtgy9Xk9aWtod20mShBACrVaLWq3GbDaj1+vzD6abAiByBOQaOJd8M6DHjJLLWmdaxYbSMTiR6t6FD8wpTdw8Xfn1iVoM/vsag6dso629gbAsKxcVjqRo8rb/KmUlnnHKYtxb/bGzu70wR7MmDeDQGSQh6P7Kr2X9CEVm38hXMdvb4t++5R3baOIhZ9unN7bU1WfcnpNfrVZh/SgOvsrbReR8Ugnbz243p+l7HsUOOH/OHqNkACuQlf91HfTX/TC5VeKYLpQRfe4cJNej43Cq+ASz/51xnLK3Ibt9K/ps3YXyX8FrJ6bOpqok0eit+8isasl7iXS1v7nAp5rMDDp5lViDiaUNqpbY4g/wUaePeLfVu2RmZnL22hkO1ZlGiouZWsDasLXsjd7L2GZj5cW/EDySCuAf/snWaLFY2Lx5M1u2bKFRo0a0a9cO5/yKSkIIIiIiyMzMxGw2YzabWbdu3S3jtGnTBnt7e4QQmM1m3N3dsbW1RafT4e7ujkKhuM3tNDMzkxZnD3Ikw40rKh1mdx3bwxKRDFZU4Vko0owAHPCpRbS6CdGrzzGuVy00qvK32jVs04jpyRlM2JHDEqMHfopMmmtzqOshaFTTjwYt62N3l0Ai18qVcdHtJlXvwveHzjO7T+sylL5oGDMziFKrcM7W03bc1wW2sVgs+KW4kqxWYG+BmqHn7+hmrNBoMb8fg+pbHzYm1KVblh57+5tFcizmm6aMN5eswJybS1boZdIvXiUrIoqchERSszLZD/h6VMI/JOCu8gcHN6fKb8v4a/AArlRyZWnvLgyYtxgbXx+y07Pw3ruJsJoh9K1Z/DTSjf2c2AJsuJpEsKs9WWYLz5wO42qOgYX1q9LMuWTLlQLodDp0Oh07Jv6Eu1MP1JpAMowZfH/0e+q712dw8OASn/Nh5JFWAAqFgvHjx2O1WklPT2f//v0cOXKE48eP4+LiglqtvpFC4t9IkkRgYCDBwcE0adIEdREKUfyDg4MDPVp2pce/LzaoikUIYgwmNkQkYZtlITEll0PXkpm/PxxnWzWju5RdEri70aVfe7r0y1OQxYmpcNG7kYoV11WXoQIrgHlv9CVHa0+Q+c7nRNcjwgjLPMnpGr406jOcWvf4fahs7RivmcV8ZzsGzdnBt6NuJnK7dirPHKbU5xV/UdnY4Ny4Ps6Nb63I1q0Iz6Bxc2Pgxh2s6d6Jq24OrBgxhAGff8feHcfwN+bgO6J4rp//MKhGJb5RnOGvs7GMaFSZZ8+EcSozh9l1AmnvWnqVtmZ/+TZOUQb0irX0f3ka0/b/TKo+lRldZqCsoHEkFY1HWgH8g0KhwMXFhd69e9OoUSPOnj1LWloaSUlJBAYGUrt2bapUqYLFYiE+Pp66devetSjI/aCUJCrrNLwcfGshjsCx69h1KbHCKIB/KM7iv+dCNGHk2cEjFOXvjXU3rtiqcBRm2v5y53OKyL3nCE07hJttAB2HP1Wocd98tRfzv93Fslgr3/7renT8ItBBJdfCjVMYrFYL53ZsITrQFzIzcMw1Ejl8OGoHT6LdK9Opd4f7Gt/NVkNlfycirqXR/mAoiWYz02sF0NPDuUTkL4jVsyeTfvoyAE+OG8e2ycdx1jZjyHNQ2612qc37sCErgP/g4+ODj8+dqyBVqlTpjvdKEx8nHQ66ipMb6H74+fz5G9/7uZV+3pziMm3DV1RK0XGqdjY2LgXXp7VYLOj3H8RozaVKt66FHtvN9aZZJD0jByfHvNz0Zl1eOomgxvf3Vv4P18+fYceCWSSGh+FXqy6dnn8F6/K/SJk9G+/MBKY3eI4uJZAXa0ADX6aGnyc+LotlnevQxqV0a+xGnDsDQGyQHzN/eZEOUZ2JqRbCi43eLdV5HzZkBfCAEJOup7rXw1G4ekGf9rTIWEvqRR0rrFX4KisLrX3J24nvB6vZyJUNW/HHlgH6bmx6dwoiK5dcSzb6f30ZLBlYBATZJ9FuyIAizdHQScHJdCs9J+9g//hbq3mpNbcfnheFtPg4di+ay+XD+3Fw86D3W2MIbtUub8f23rtEKu2w//VHdjlWpeXX2zjwYediz3U2Op15m/Pexnt4OJf64g/QaOAwli77jqanT2PpYGR54CamvfU92hJ2NX3YKf8TRZlCUc/XieORqTxIbrt3QqvV8mPLm9Wm/pr8fTlKUzDxEdfwj897Kw8L28fZqG2cS9tPdPZlss3paBRaPHSVqeFYkw6eV2nWt/Bv//8w65U8982YfCtYdlrMfcttyMlh9x/zmf/Oq1w7dYzWg4by/A+/ULN1+1vMdU3ffhXtniNkaWyJTdcTOHYdelPRYhOMZiuTt1yi30/7UAJD2lVhdtuyMVG2adWJHyevw7Z2N/Qn2zPE6TNstSXnafSoIO8AHhCeae7P2L/OcCk+i+ACwu0fNBZc3YMNjuSi49LZ8pbmdrwCqmFbNQuDqgFtg7rg1rAK3sFBaG0KejMvngLzcLXHHgOZOltGffYJrZ1O4FYPNKaaRR7LYjZxZttmDqxYTE56GrXbdaLN08NxcL2zW3NVD3vOTehGnU83A1Dz443sGtOBADe7O/b5h3Mx6by3/DShsRk83siXTx6rjbNt2S7ACoWCJ78dX2xHBJlCKABJknTAbkCb3/5PIcSnkiQ1AH4B7IFw4BkhRMZ/+lYGfgMqkefNPFMI8WP+PVdgKRCY33+QEOLOuWRlAFApH44P+vFjGnLJc398Yexj5SzN7ShUSl77emepz/NclVN8X70/uVvNZBnTcAOqVv2g0P2F1cqF/bvZt2wR6fFx+NasQ//3P8a7WuFKUtpp1YRP7E3IF1tIyjLS/rudzHk2hM61Cs6bb7JY+WnHFaZvv4KLnYZZw0PoWs459uXFv/jcMxJYyvvt2gkhsiRJUgN7gVHANOA9IcQuSZJGAFWEEB//p6834C2EOC5JkgNwDOgvhDgvSdK3QIoQYqIkSWMBFyHEXT/5JRUJ/CCRazQzeOZBLsZl4mSrZu8HnVArH2zLnRCCmh+uw4DEGNUSRn62ACpogZ5SRwiQJEwmE+cuHUMjcqhdt1Ohu+9aNJeja/7CI6AKbZ9+lsCGTYq9IL6w4AjbQvPcnkd1rs7bXW8155yPyeC95ac4H5tB/4Y+jO9bp8zf+mWKR7EjgUWehsjK/1Gd/yWAYPJ2BgBbgE3Ax//pGwvE5n+fKUlSKOALnAf6AR3ymy4AdgKFf/V5RPh200VORaXj5ahlxWutHvjFH/55Y5N4zGpg5BcVp/RluZC/WKvVahrWKXwdXoBrJ45ybO3f1Gnfhe6vvoV0n0p0zrNN+WnHFb7bdJEft13m5PU0FoxohsliZcbOq0zddhlnWzW/DmtC9zrl4w0nU7IU6gxAkiQleW/v1YCfhBCHJEk6C/QFVgEDgbuWQZIkKRBoBPyTRcwrX0EghIiVJKnALFuSJL0MvAzg7+9fGHEfKqJTc6nkqGPvBx1RPQSL/z8YhZW4jLy6DLINt2iY9Hp2/zGfk5vX4RlQlQ7Pvnjfi/8/jOxYjXq+jgyfe4RdlxKp9/EGAt10nInLoW8DHyb0rYOLnfzW/7BQKAUghLAADSVJcgZWSpJUFxgBTJUk6RNgNWC8U39JkuyBFcDo/54TFGLumcBMyDMBFaXvw8Dm8/F0q+31UC3+V8JiEJKCXIWaYVOXsdPiRoBLLHufK142ykeFc7u2cXbHFqIvnkdYrTTq8RitnxqK1vbeh7ZFoV0NT/Z90InW32wn02Sl04b5jJw0kR51ve/dWeaBokheQEKINEmSdgI9hBDfkx+RLklSDaB3QX3yzw1WAL8LIf761614SZK889/+vYHbcy484pgtedGym8/H3/aW/CC/Nfu6OeNtljjrWAdiQIme6ARnTk2bQ4M3Xyhv8SoEZqORiwf2kBwVib2rG8Iq2PnbLJw8vWj62OMENmhM5Tr17z1QMfF1seHS5z2Y9Mpn9HpzGMHy4v9QUhgvIA/AlL/42wBdgG8kSfIUQiRIkqQA/keeR9B/+0rAHCBUCPHfqh+rgWeBifn/rrq/R3n4UCokqnnacyUhi+ofbaCOjyMOOjUalYJDYcl4OGip7uWAg1ZFQ39nutb2wtvp7gFE/ygOq1WgUNxUIEazlT2XE5m/P5w9l5NK1c5r42RL25TFHHRsQH2HHK6muRFqF8iF6As0uHf3hxYhBBFnThK6ZwfXTh4jNyMdhVKF1ZIXLe1ftz5PfPQ5ijLKc6NRK/lw7oQymUumfCiMF1B98g5pleQFji0TQnwmSdIoYGR+s7+AD4UQQpIkH2C2EKKXJEltgD3AGeCf8j3jhBDrJUlyA5YB/kAkMFAIkXI3WR5FLyC9ycJHK88SlpRFQoYBpUIiLcdIuxoeZBvMxGUYSM02EpehR62UGNYikLc6BCIUKmLSc/nreDRXErI4FBGF3qDm37F/NmoldlolydlGCvoY+Lvacj0158a9vg18GNysMo0qu2Cjub9FSAhBVOgJ3pp+hBOOfgRlh7Hu25fQ2eju3fkhJDU2mq1zZhB55iQaGxsC6jWiUY8++NWuR/TF81zcv4cWjz+FnbNc2Fym6NzJC+iRLAjzsCGEICwpm9l7wlh65DrW//yX1vN1IkfEEZ2RxPDG7QlPyibbaKZWJUeyjWbSc01sOBvHx71r80wLf64mZDN9x2UkJEJjMwhLyr5lPLVSolkVV15qW5X2NTyKbYp6e/hzrPQZiJNFYlKrSnQZ0Li4v4IHFmNuDqe3bmTf8t9RKJS0fmoodTt0QWNjW96iyTxEyArgEeFCXAbLZ36NR6Ne2LlXJsjDnlbV7r8malKWgTPR6WTkmjgfk8Ha07FEp+USEuDCu92CaRnkVuQxX/l8LJuy89IhhDido161s3g4vkZvd0cCmtS5b5krOiaDnmWfjSPuyiX869anx8h37hq5KyNTXGQFIFOiGM1Wlh29zrTtl4nPMFDZ1YYJfevQMdjznjsCs8WK0WJl6Q+TmJBS97b7fpjY/F5TbN0fropOwmolPTGB3Ix0Is+eInTvTpKjIukzeizBLduUt3gyDzFySUiZEkWjUjC0RQBPNvFj1u4wJm+9xIj5RwkJcGFAY1+61vbCaoVL8ZkcvpZCZEoO2QYz15Kyb5iUFj33LMN/ncoRcyB425GhjCY6tQ5qYwZ6lQ0PkxHk2omjbJs7g/SE+BvX3Pz8GfDBp1Rt3LQcJZN5lJF3ADIlQly6ns3n45i3L5xr/zkzUCok/FxssNOoiEzJIctwswZA+MSb3sMNvviYjOxGLOhiT7vORal5VXHJzcpk8y9TuXLkAPZu7rQYMAhbZxfcKwfgUunOdSdkZEoSeQcgU6pUctIxvGUgw1oEEBqbybbQeA6Hp/BS26o0DnDBXpv3URNCcDoqnX4/7eP9HrcmLBuQm8bvQolb7EmKVvSwYpGdlsqZ7ZuxmIyc2bGF3Ix0Wj81jCZ9+qPWaMtbPBmZG8gKQKZEkSSJ2j6O1PYpuCi8JEmEJeWllmpbzeOWe8/2aMtv6xR8d87Ip0mpVHGv2C6P+uws4i5fxGKxoLWxxWTQs2fxAhIjrt3SrvVTw2jxeMmVeJSRKSlkBSBT5gTm55tffuw6tX0cUeYHpFVpOwjVupXsFI3YvP4cMcMr7sFodloqi8a9TVZy0i3X7V3daPHEYKqFtMDBzZ20+Di8q1WsOs4yMv8gKwCZMqdhZWeeblaZ3w5EcOBqMqO71KBn3UooFBIjW2UyZb8bbY27sFpaolCWTdQr3IyS3rJ5K39HJ7IisBYfh/5N38hOVJrQHLVWS056Gud2b+fgiiUY9bnU7dgVn+BaZKekkJ2eStO+T+DofjOvoa2Tc5nJLyNTVGQFIFPmSJLEVwPq0ba6B5O3XGLkH8d5sokfbaq58+OBvHgCY7qKKUP6AeATXJvB4yeWWMbLgtjx/WpS95xhT82/edI1kkaGTqwIrEV8YCyxF8LY/9V6kCDmUihWiwUnTy8Gf/YtHv6BpSaTjExpI3sByZQrFqvgh62XmLb9CgCN/Z15tb0vyTtXE7N78412VRs3pftro7F1dCqxuU3ZeiS1ApVGw++jF5Cmr8xpx7fYX0dimfdbuDTuzB/vv43IzquV6145gIAGjanVuj0egVXKLCePjMz9IgeCyVRotl+IJyHDwGMNfLD7l8dQRmICV44cZM/i+dg6OvPEuM9w87tr6YkbGM0WRvyyk8cauzCweYNbzEkJRy5jXBHHdf0FTubsIic9DYGCiEGepKtymfPYfNQKNQCGnGxMej32rkWPdpaRqQjICkDmgSY+7Ap/TRyP1WxmwNhP8alR68a9lJhoFq+Ywg63Sywb/DeKfFPR9BkL+T7CFau9CmNrL85Vd8PFx4fdv8/n9MYNPF55FAcy1xKZdA6AStVqMPDjL9Ho7p5RVUbmQUNWADIPPGnxccx560UA3v5j1Y03+lMNG3KoimBafystc3OZGZd4o894my/5f3v3GiNVfYdx/PusVFJrAUmXBYI3FEptIhrAapDKzWo08dag9RUJ0VbwEkkg0lhU3hi0oPHywmsbXxiqaFSMt6jJojFCiy0opFCC0dR4QSWCVKWp/flizpgFZnbZOXP2zJnzfJLJnJkz/7O/J7N7fvs/O3vOfZOnctKXW7ih4y7WbzwHffk1U44fw2kXzWHYyNG8+/e/0XnscQztzPfi5mZZqdcA2ucyU9b2hnWN5PzrFgOw5o5bqf7y8tXgMfx40BWM4AiWfrb/GcWXzp/Dx7NO5cYjb2PQkbuYeuYqhozo4twFCxk+egwdHR2cOPkX3vlbKflTQFYoE6aexZa1r7Jjw3q2vrGWn505nbcm/o7/fbOOe8et5ui5+183unrUf+ast+nuPplRw69i1uKrD96wWQl5BmCFc/GSm+kaO47n71nBnk93sm/3/Xy7bxOHD65/uWlJzJjxDhMmeudvVuUGYIXT0XEY40+fCsAj1y0A4IdDhnLCpINPLW1m9fkQkBXSlAt+Tecbw/hBx2Ceev8uFjz4aN4lmRWOZwBWSJI4YvZoguCaR1fnXY5ZIXkGYIXV9auTinzWaLPceQZgZlZSbgBmZiXlBmBmVlJuAGZmJeUGYGZWUm4AZmYl5QZgZlZSbgBmZiVVqOsBSPoUeD/DL/ET4LMMt5+HdsvUbnnAmYqiyJmOjYjOA58sVAPImqQNtS6aUGTtlqnd8oAzFUU7ZvIhIDOzknIDMDMrKTeA/T2QdwEZaLdM7ZYHnKko2i6T/wZgZlZSngGYmZWUG4CZWUmVqgFIekzSxuT2nqSNyfPHSfq6x7r76oyfI2mLpP9LaomPgzUh03BJL0vantwfNaABatdUM1OP9cdI2itpUZ3xEyW9KekdSc9KGjIghfeiCZlOkbQuGb9B0mkDUngvmpCp1/F5SJspec21krYl+4rbMy86hVJdESwiLqsuS1oJ7O6xekdEnNLHJjYDlwD3N7+6xjQh0xLg1YhYLmlJ8viGphfaD31kArgTeKGXTTwELIqItZLmAYuBpU0vtB+akOl2YFlEvCDpvOTx9GbX2R9pMx3C+AGXNpOkGcCFwMkRsU/SiEwKbZJSNYAqSQIuBWb2Z1xE/DMZn0VZqTSaico36/Rk+RGgm5wbQFWtTJIuAt4F/tPL0J8CryXLLwMvkXMDqEqRKYDqTGYo8GFGJfZbikx1x+ctRab5wPKI2AcQETszLDO1Uh0C6mEa8ElEbO/x3PGS/iFpraRpeRWWQqOZuiLiI4DkvpV+Y9kvk6QfUWlOy/oYtxm4IFmeAxydWYX912im64E/Svo3sAL4fZZF9lOjmWqObxGNZhoPTJO0Pvm5m5Jxnam03QxA0ivAyBqrboyIZ5Lly4FVPdZ9BBwTEZ9LmgQ8LennEbEn43IPiTN9bxlwZ0Ts7WMWNg+4W9JNwBrgv00ouU8ZZ5oPLIyIJyVdCjwMzG5C2b3KOFPVgeMzlXGmQcBRwOnAFOBxSWOjVT9vHxGlulF5gz4BxvTymm5gcqPri5QJ2AaMSpZHAdvyzlMvE/A68F5y+wLYBVzTx3bGA3/NO0/aTFSORVf/b0fAnrzzNON9OpTv3SJlAl4Epvd4vAPozDtTvVvbzQAOwWxga0R8UH1CUiewKyK+lTQWGEflWF9RpMm0BpgLLE/un6nxmjwclCkivj+MJekWYG9E3HvgQEkjImKnpA7gD0DNT0DloOFMVI75n0Wlkc8EWuVwSZpMNce3gDSZnqby/nRLGg8cTgufQbSMfwP4DQdPN38JvC1pE/AEcFVE7AKQ9JCSj3xKuljSB8AZwHOSXhrAunvTcCYqO/6zJW0Hzk4et4Jameo6INPlkv4FbKWy4/xzBvU1Ik2mK4GVyft5K/DbDOprRJpM/R4/QNJk+hMwVtJm4C/A3EimAq3Ip4IwMyupMs4AzMwMNwAzs9JyAzAzKyk3ADOzknIDMDMrKTcAM7OScgMwMyup7wBKHwuvCiVwXAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 3 of 4\n",
    "#now visualize the receivers\n",
    "for pp in range(len(precinctReceivers)):\n",
    "    p = precinctReceivers[pp]\n",
    "    plotPoly(vtdGeom[p])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "5d698e51-7b2d-40b8-b932-d22f20f09bb0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#now visualize the TRACT receivers\n",
    "for tt in range(len(tractReceivers)):\n",
    "    t = tractReceivers[tt]\n",
    "    plotPoly(tractGeom[t])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "c17c1582-29d1-41bc-8f56-b32ac70747ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have finished slicing  67339.0 people (VAP of 56252.0 ) and  39990.2460227931 voters out.\n"
     ]
    }
   ],
   "source": [
    "#now, cut and redistribute\n",
    "cutPop = 0.\n",
    "cutVAP = 0.\n",
    "cutHisp = 0.\n",
    "cutBlack = 0.\n",
    "cutTrump = 0.\n",
    "cutBiden = 0.\n",
    "\n",
    "for ct in range(len(cutTractList)):\n",
    "    t = cutTractList[ct]\n",
    "    cutPop += tractPop[t]  #sum up the cut pops\n",
    "    cutVAP += tractVAP[t]\n",
    "    cutHisp += tractHisp[t]\n",
    "    cutBlack += tractBlack[t]\n",
    "    # Now we zero out the pops in the cut tracts\n",
    "    tractPop[t] = 0\n",
    "    tractVAP[t] = 0\n",
    "    tractHisp[t] = 0\n",
    "    tractBlack[t] = 0\n",
    "# Now distribute the pops among the receivers\n",
    "ntR = len(tractReceivers)\n",
    "NTR = float(ntR)\n",
    "for rt in range(ntR) :\n",
    "    t = tractReceivers[rt]\n",
    "    tractPop[t] += cutPop/NTR\n",
    "    tractVAP[t] += cutVAP/NTR\n",
    "    tractHisp[t] += cutHisp/NTR\n",
    "    tractBlack[t] += cutBlack/NTR\n",
    "\n",
    "#             now do the same for the precincts\n",
    "for cp in range(len(cutPrecinctList)):\n",
    "    p = cutPrecinctList[cp]\n",
    "    cutTrump += vtdTrump[p]\n",
    "    cutBiden += vtdBiden[p]\n",
    "        # Now we zero out the pops in the cut precincts\n",
    "    vtdTrump[p] = 0\n",
    "    vtdBiden[p] = 0\n",
    "\n",
    "# Now distribute the pops among the receivers\n",
    "npR = len(precinctReceivers)\n",
    "NPR = float(npR)\n",
    "for rp in range(npR) :\n",
    "    p = precinctReceivers[rp]\n",
    "    vtdTrump[p] += cutTrump/NPR\n",
    "    vtdBiden[p] += cutBiden/NPR\n",
    "print(\"I have finished slicing \",cutPop,\"people (VAP of\",cutVAP,\") and \",cutBiden+cutTrump,\"voters out.\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "55419803-eaaf-4181-8848-d8fdad0205b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8EdmulFpSy7me7j0LmAWQnJzcqNiuFTHBW8KLJD2nmLjwQDak5fDGkn0Uljn45/lDapifSh1OLn51BVsy8rAI3DOtXy1X1ATbrXx750Q+Wp3Kb3uP8tHq1Mq6qQPqTkajab9UKABtAqqdBs39lVI5IrIImK6UegY4E0BE+gHn1NImw3zPFJEvgNEYpqMjIhKvlDokIvEYs4tmoSJ2fnObgNYePM5F/11BoM1CqcNVWf77V1fw8c1j6RN7wlSRXVDGlow8xvTsyKPnDfbb9Ql/ISrUzq2n9ebW03ozc/ZKVu49xpe3T2BQlwhfi6bxU7QJqH7qNYqISIw58kdEgoEzgO3miB4RsQCPYHgEVW8bKiLhFZ8xFMZms/pr4Brz8zXAVyf1TerAafbFzR0OenemsVRS6nDR37RLR4fZOVpYxhnPLeGFn3dS5nCReqyIa99aBRihkQfG606sIQQHWClzurj27VWUlOt47xrPhGoTUL14MwOIB+aIiBVDYcxVSn0rIneJyO3mOZ8DbwOISBfgTaXUDCAOw2RUca8PlFLzzDZPAnNF5HrgIMY6QrPgaqE1gPVmrttpg+J44+pkAApKHfzv1wMs353NCz/vYt7mw0zqF8OuzAKuGJ3AafXk09XUpGLnsk72pKkLu81CoM2iw0HUgTdeQBuBUzyUvwi86KE8A5hhft4LDK/lukeB0xsob6NwmT1Fc84AlFLkmh3Tn6aeiKQZFmirNF38tOUwj3y5mVlm2GPt0tg4ukUFs2o/zBzbo9ZdzhoN6HhA9eFnfjHNQ8VIsTnjgbz32wG+23SIPrFhDO3medfJmYM789UdE4gOsyMCI7pHNZs8bZWSciefr0sHjExm1VNmajTuhAVpBVAX7UIBVLqBNkP/n1dSTl5JOX8zk5xP7hdT5/nxkcEse3Aqax+ZxuieOrJlQwm0Wbgs2fA4fvrHHUx5ZhEvLdjlY6k0/kqoXYeErgv/3QHUhFSuATSxCai4zMmp/15IbrFp+jm9L/d64c4ZFGDVpotGIiL8+/fDuGNqHzJyinlj6V7+s3A3fxjbo0kypGnaFnoGUDftYgbQXGsAy3dnV3b+0WGBXD2uR5NeX1M7CR1DGNOrEw9MH0CZw8XHbnsDNJoKwvUaQJ20DwXQxGsADqeLT9ekccO7KQCc1j+Gt68dVSOhiab56RcXzqjEKL5an+5rUTR+SKhWAHXSrkxATTUBeODTjZULkQD3n9WfwV10uEFfsCUjlx2H8ylxuFBK6ZwBmiqEBdn0RrA6aB8KoAlNQAePFvHF+nREjFj7PTqGVG760rQ87644QF6JgycuGqo7f00NwgNt5OtF4FppHwqgCUxADqeLN5ft48kftmOzCMsenKrTE/oBk/vH8HFKKnER2vymqUlooI1Sh4typ4sAa7uweDeIdqEATtYN9LmfdvDSL7srj++Z1k93/n7CyB7GXordmQVMHRDnY2k0/oZ7PCB/j3zrC9qFAjhZE9DmDCPR2V9nDGTmOL371F9wOF3c+eE6RGBUot5ToalJZVKYEq0APNEuFMCh3BKARvmJr9iTzS/bM4kOs3PjpF5NLZrmJNh/tJBV+45xat9okhI6+FocjR9SOQMo0+sAnmgXRrFShxExsqGJoYvKHNw3dwMA5w3v2uRyaU6OXtFhzBjamaW7srnwvytYsjOLA0cLWb47m3eW76Pc6ar/Ipo2jU4KUzftYgbgdCksYmSXagj/+/UAGbklvPaHEUwfEt9M0mkai8UivHLlCD5dk8a/vt3K1WaI7QoWbM/0mIxH037QeYHrpl0oAIdLYfMyFnSZw8Uv2zNxuhTLdmfTNzZMd/5+jIhwSXIC04d0JuXAcQ5kF1LicBEcYOX/vt/GlGcWkdwjivOSujAwPoLs/FKW7Mrm3OHxLZqCU+MbdFKYumkXCsDpUl65gG5IzeG299eSnlNcWXbdhJ7NKZqmiQgPCmBK/1jof6Js+pDOfL42nU/WpPJ3M1hfBdsO5fHl7VoBtHW0Cahu2oUCcDgVtnoUgMPp4vxXlgPw2HmDCbZb2ZNZwLUTEltAQk1zEBcRxK2n9ebmSb3YmZnP4dwSAm1WvliXxrzNh/XO4XZAZVYwPQPwSLtQAC6loI6/85yiMl42/fxP7RvNNeMTW0YwTYtgsQgDOkcwoLORevNIXglzU9J4e/l+rpuoZ3htmTCtAOrEm5zAQSKySkQ2iMgWEXnMLB8uIr+KyCYR+UZEaiS2FZEEEVkoItvMtne51T0qIukist58zWjar3YCq0Uq9wJU55OUVJL+OZ/Zy/Zxat9o3r52VHOJofETzk/qwpT+MTz14/bKNJ6atonVIoTYrdoEVAverIyWAlOVUsOBJGC6iIwF3gQeUkoNBb4A7vfQ1gH8WSk1EBgL3C4ig9zqn1dKJZmv70/mi9SFAJ66//3Zhdz/6UYAvrx9Au9eNxqb3i7e5hERHvndICKCArjov8v5v++3caywzNdiaZoJHRG0durt7ZRBgXkYYL4UxnLbErN8PnCxh7aHlFJrzc/5wDagxR3qRUBV0wAul+K6d1YDMPfmcSQldND24HZE75gwfv7zZC4f3Z1ZS/aS/Ph87v9kg9470AbROQFqx6vhrohYRWQ9kAnMV0qtBDYD55mnXAIk1HONRIzk8ivdiu8QkY0i8paIeEyQKyI3iUiKiKRkZWV5I66na6Dc5gBKKSY/s5C92YVMHRCrUzO2UyKCAvi/C4fy7Z0TuXpcIp+sSePvX22u1VyoaZ3orGC145UCUEo5lVJJQDdgtIgMAa7DMOmsAcKBWufQIhIGfAbcrZTKM4tfBXpjmJUOAc/Wcu9ZSqlkpVRyTEzd+XZrvT9VZwDfbzpM6rFihneL5I2rkxt1TU3bYUjXSB49bzC3T+nNh6tS+fvXm30tkqYJCbXrnAC10SCDt1IqB1gETFdKbVdKnamUGgl8COzx1EZEAjA6//eVUp+7XeuIqVhcwBvA6MZ9BS9ws+yk7D/G7R+sBeA/V45osixhmtbPfWf259S+0bz320HKHNoU1FYIC9I5AWrDGy+gGBHpYH4OBs4AtotIrFlmAR4BXvPQVoDZwDal1HPV6ty3116IYVJqNkodLv704Tp+/9qvALxxdTIJHUOa85aaVsbe7EJW7DnKtEFx2G3aGaCtoNcAasebX3k8sFBENgKrMdYAvgWuEJGdwHYgA3gbQES6iEiFR88EYCYw1YO751OmC+lGYApwT9N9rap0MqOAfr0hg7OHdGbNI2cwbZCOHa+pytvL92GzCE9cNNTXomiakNBAbQKqjXo3gimlNmIs3lYvfxF40UN5BjDD/LyMWrZgKaVmNlTYxnLDxF70ig4j9XgRf9ShHTQeOJJXwpaMPBI6hhAdprOLtSUqFoH1zu+atIudwBaLcIYe8Ws8oJTiwc82MjclDYC//W5QPS00rY2wQBvlTkWpw6WTOVWjXSgAjaY2Fu3IYm5KGpclJ3DDqT3pGxfua5E0TYx7RFCtAKqiFYCmXfPR6oPEhgfy+IVDdNLwNop7PKBO2rxXBf2L17RblFKsOZDDqX1jdOffhqmICKpdQWuif/WadsvRwjKyC0rp3znM16JompHwIJ0Upja0AtC0Wyr8QQJt2i7cltEhoWtHKwBNuyUsyIbdZmHVvmMUlenOoa2ik8LUjlYAmnZLoM3KzLE9+G7TIcb83wJeWbgbVT1srKbVU2EC0gqgJloBaNo1j5wzkE9vGcfIHlE8/eMOFu7I9LVImiZG5wWuHa0ANO0aESE5sSOzZibTKzqUf327jZJyp6/F0jQhIXYrInoR2BNaAWg0gN1m4b6z+rMvu5BftutZQFtCRAiz28jXCqAGeiOYRmNyxsA4YsIDefKH7WTkFHNa/xh6x4Tp+DFtgLAgmzYBeUArAI3GxG6z8PylSTz2zRYe/24bj3+3jY6hdgbGh9MlMphgu5WiMid2m4VbJ/fW4cRbEaGBNgq1p1cNtALQaNyY2Dea+fdOJu14EYt3ZrExNZeth/LYk5lNQamDyOAA0nOK+WpdOpckJ3D56AQGdI7wtdiaeggL1ElhPKEVgEbjgW5RIVw1pgdXjalZd/BoEY9+s4UPVh7k/ZUHeOGyUzhnWHzNEzV+Q5hOCuMRvQis0TSQ7p1CeOvaUax8+HSSEjpw54drefzbrezOLPC1aJpaCNNJYTziTUrIIBFZJSIbRGSLiDxmlg8XkV/NrF7fiIjHebCITBeRHSKyW0QecivvKCLzRWSX+R7VdF9Lo2l+okLtzLluNBckdWX28n1Mf2EJy3Zl+1osjQf0IrBnvJkBlAJTlVLDgSRguoiMBd4EHlJKDQW+AO6v3lBErMArwNnAIIw0khUZNx4CFiil+gILzGONplURYrfx3GVJLH9wKg6X4udtR3wtksYDYYHaDdQT9SoAZVAxtw0wXwroDywxy+cDF3toPhrYrZTaq5QqAz4CzjfrzgfmmJ/nABc05gtoNP7Akp1ZAEzuH+NjSTSeqDAB6VAfVfFqDUBErCKyHsjESAq/EtgMnGeecgmQ4KFpVyDV7TjNLAOIU0odAjDfYxssvUbjJ6QeLwJgZI+GWTJdLsVT87Yzd3Vq/SdrGk1YkA2XgmK9y7sKXikApZRTKZUEdANGi8gQ4DrgdhFZA4QDZR6aetpB0yAVLCI3iUiKiKRkZWU1pKlG02KcNbgzAJ+auYW95dn5O/jvoj088NlG5m0+1ByiaXCLCKrXAarQIC8gpVQOsAiYrpTarpQ6Uyk1EvgQ2OOhSRpVZwbdgAzz8xERiQcw3z3uv1dKzVJKJSulkmNi9PRa458M69aBoV0j+ee3W3nky01euRzOXZ3KKwv3cNGIrpzSvQN3f7yejWk5zS9sOyRch4T2iDdeQDEi0sH8HAycAWwXkVizzAI8ArzmoflqoK+I9BQRO3A58LVZ9zVwjfn5GuCrk/geGo3P+e9VIwB477eDDPnHjwx/7CdG/Gs+F7yynL9/tZncovLKc5ftyubhLzYxqV8MT108jFkzk4kOC+T6OSlk5BT76iu0WXRSGM94MwOIBxaKyEaMDn2+UupbDI+encB2jFH92wAi0kVEvgdQSjmAO4AfgW3AXKXUFvO6TwLTRGQXMM081mhaLQkdQ9j3xAzev2EM145PpFdMKGN7dcRus/Durwf400frUEqx80g+t763hj6xYbxy5SnYrBZiwgN569pRlJQ5ue6d1bqjamJ0UhjPSGtaFU9OTlYpKSm+FkOjaTDvLN/Ho99s5enfD+OFn3dR7nTx5e0T6NIhuMp5S3Zm8cd3VjOpbzRvXJ2MTSerbxI2p+fyu5eXMWvmSM4012vaEyKyRimVXL1c/7o0mhbgyjE9CA+0cf+nGzlaWMpb146q0fkDTOoXwz/PH8zCHVk8/t02H0jaNtEzAM9oBaDRtAB2m4Wnfj+MDiEB/OeKEQzpGlnruVeN6cENE3vyzor9zFmxv+WEbMNUrAHocBBV0cHgNJoW4uyh8Zw1uDMWS/35Bf4yYyD7jxbx2Ddb6N4xhCkD9DaZk6EiL7DeDVwVPQPQaFoQbzp/AKtFePHyJAZ0juCOD9ay7VBeM0vWtgm0WbBaRO8DqIZWABqNnxIaaGP2tcmEBdm4/p3VZOaV+FqkVouI6IigHtAKQKPxY+Ijg3ngrAFk5Jbw4SodLuJk0AHhaqLXADQaP+Zwbgn/nredrh2CuWpsd1+L4xXpOcX87cvNbEjN4YyBcTx58VC/yKscFqhDQldHKwCNxk8pKnNww7urKSx18Nlt44kOC/S1SPWy9uBxbno3haIyJ0VlTj5OSWVkjyguHeUpVmTLEhak8wJXR5uANBo/xOVS3PvxBrZm5PHylae0irzDy3dnc8Ws3wix2xjds2Nl+b++20pmvu/XL/QMoCZaAWg0fsiz83cwb8thHp4xkKkD4nwtTr1sycjlhjkpJHYK5bJRCSzacSJyb36Jg1+2eYz12KLoNYCaaAWg0fgZn69N45WFe7hidALXT+zpa3G84onvtxNst/LKVafw8i+76B0TWlkXaLNUmRH4Cu0FVBOtADQaPyJl/zEe+mwTY3t15LHzhvjF4ml9rDlwjGW7szl7SGd+9/IySspdvHLVCM4d3oXgACtPXDSUXjFhvhaTUG0CqoFeBNZo/ITUY0Xc/L81dOkQxGt/GInd5v/js7ySch75cgsdQ+0UlTlxueC1P4xgQOcIXrwsCYWxqc0fMBaBnbhcyusNeW0drQA0Gj8gv6ScG+akUO50MfvaUXQIsftapCrkl5Tz3cZDHC0sI7e4nHKni06hdj5dk0ba8WJmXzuKhz/fxLRBcUwfEg94v+u5pahIClNY5iA8KMDH0vgHWgFoND7G6VLc9dF6dmcVMOePo+ntB+aS6ry/8iBP/rAdgKAACxYRisqc9IkN470bxjCoSwTpOcVcOcZ/9yq4RwTVCsBAKwCNxse8/MsuftmeyeMXDGFi32hfi+ORDak5dO8Ywk/3TCIowEpBqYOF2zPp3zmcPjFhzF62D8AvFntrIyxIRwStjlYAGo2PWbHnKEkJHfjD2B6+FqVWNqXnMjyhA0EBVnKKyrj41RXsySoEDNNKfqmD5B5RjEr0XwVQYQLK1wvBlXiTEzhIRFaJyAYR2SIij5nlSSLym4isF5EUERntoW1/s77ilScid5t1j4pIulvdjCb/dhpNK6C4zElUiP+aJI4XlpF2vJhhZg6DN5buZV92Ic9eMpxnLhnOeUldSO4Rxf1n9fexpHWjk8LUxJsZQCkwVSlVICIBwDIR+QH4J/CYUuoHs/N+CjjNvaFSageQBCAiViAd+MLtlOeVUs+c9LfQaFoxRWUOQuwhvhajVjal5wIw1FQA+7ILSewUysUjuwHwe/Pd39FJYWpS7wxAGRSYhwHmS5mviv3pkRiJ4evidGCPUupAI2XVaNokRWVOgu1WX4tRKxUKYLCpALpEBpN2vLjVjaQrk8JoE1AlXjkai4hVRNYDmcB8pdRK4G7gaRFJBZ4B/lLPZS4HPqxWdoeIbBSRt0QkqpZ732SamFKysrI8naLRtGqKypyE+LMCSMslsVMIkcGGmWraoDjKnC4W7ag/vEO509Xc4nmNNgHVxCsFoJRyKqWSgG7AaBEZAtwK3KOUSgDuAWbX1l5E7MB5wCduxa8CvTFMRIeAZ2u59yylVLJSKjkmJsYbcTWaVoNSiqIyBzaL/2762pyRWyWHcXJiR2LCA7l37gZufDeF9ak5AOzNKuDXPUcBI5jdbe+voe9ff+CHTYd8IXYNQgMNJatNQCdo0K9OKZUDLAKmA9cAn5tVnwA1FoHdOBtYq5Q64natI6ZicQFv1NNeo2mTiAhWi2D10/6/qMxB2vFi+seFV5ZZLcKHN47hqjHd+W3PUS5+dQXzNh/mjOcWc8Ubv5GRU8w7K/bz/abDACzfk+0r8asQaLNit1l0QDg36l0EFpEYoFwplSMiwcAZwL8xbP6TMRTCVGBXHZe5gmrmHxGJV0pVDA0uBDY3WHqNppWyO7OAlxbsol9cGCXlLmLC/SPWf2Z+Cc/9tBOAoAArpQ7DhFM9lk+f2HD+ce5gRvaI4o4P1vGfhbtwKaPutvfXcsQtfeXEPv4zc9choavijRdQPDDH9OKxAHOVUt+KSA7woojYgBLgJgAR6QK8qZSaYR6HANOAm6td9ykRScJYTN7voV6jabPc/L+USj/6ntGhXDTCPzxpft1zlI9Wp9Ip1E6500VJuYtQu5Vh3SI9np96rBiA9OPFTO4XQ++YMD5dk0peiYPI4ADySspZl3qc6UM6t+TXqBUdEbQq9SoApdRG4BQP5cuAkR7KM4AZbsdFQCcP581sqLAaTVtgT1YBe7IKsVmExQ9MITY8kAA/sQE5zWH8Z7eOJzHaCOmslKo1KmmRmWGrf+dwdmcW8PrMkUzuH8M1b61iQOdwukYF8/rivfSPC/cLJRcaaNOLwG74x69Oo2knLN+dzfQXlgBwWv8YukQG+U3nDycUgHsEz7pCUucVlxMeaOOacYmk5xTz696jTOobzee3jee/V43gwekDCLVb+c/C3c0uuzeEawVQBR0KQqNpIYrKHPzl803Ehgfx+W3jiYsI8rVItaKUd+fFRgSRX+pgT1aB2c6YLYzofsKr+7YpfXj6xx3879f97D9axJmD4hjTq4ZRoEUIC7L5RXpKf8F/hh4aTRtnfWoOB48Vcdfpff228482F6OzCrzrJMf3NjryZ37ayYjuHRjfu2Ywuz9OSGRgfAR/+2oLs5ft4+b31rA/u7DphG4AOilMVbQC0GhaiAgzBHFEsP/G/elsKqbDuaVenZ+U0IH7z+rP/Wf15+ObxxEUUHNDW4jdxoc3juHOqX2YNdNYNrx+zmpKyp1NJ7iXhAXaKCht+fv6K9oEpNG0EF07BANUmkv8kUoFkOfdDEBEuH1Kn3rP6xBi589nGsHiXEpxy3trWbnvGJP7tayLaHiQjYLS8ha9pz+jZwAaTQsRFWqnc0QQ+3xk/vCGDiEBRATZ2JqR1+hrlDtdlDqcKKUoKHWQnlNcWbc5PZeXFhgLwgu2HantEs1GqN1GSbkLhx+FqPAlegag0dSFUpB/CCwBYLODLQisdmhEsvbN6blkFZT6dehnEWHKgFgW76w/zo8ndh3J5/xXllNUdsLMUuHuuvbAce77ZEOlCcwXQdlOJIVxEhmix79aAWg0dbH0Gfjl8Zrl1kBDGdgC3V6mcnArV7ZAHGLnaAls3ZvPv4LtzHAlwuJw4xyrW1ubW1tvrm9pngByQ7tG8tX6DDLzSoht4GK1mKki4yICuWxUd9an5rBkZxYPfbaRpbuyGZUYxatmwvtQe8t3P5VJYUrLifRjRdxSaAWg0dRGSR6seBl6TITBF4CzDBwl4Ch1e5V4KC+BskIKc7LIPJ6LxVmKXRycKeVEiBPLujJwNcHo12KrpjDqUxqelEx15RPEmJJiTrMcIGcLxHbtVPM6AcEQHOVxFtQnNozeMaF0jgzi3mn9KCpz8Oe5G5i35TBnDorj5StPIdDmu8inOiJoVbQC0GhqY9UsKMmFsx6HLjU2w9dJYamDM55bjDVImDm2BxHBAZw7vAsWswPC6QBnNUXiKHUrKwFHhWLxpGSqKx/3892uU5pftdy9jbPMo+xDgXfswI91fMEpj8Dk+z1WTewTzXsrD5JbXE5kcACv/mEkxwvLiAwOwGJpuOmsKdF5gauiFYBG44nSAvj1Feh7ZoM7f4AXF+ziUG4Jn94yjmRPeXKtNuNlD20CYRuJy1VVsZhKY86S7Xy5Zh+fXH8KNldZTeWz7Dk4uMLjJb9an84Hqw7SMzqUAOuJzj4q1N5S36pOwsyQ0DopjIFWABqNJ1LeguJjMMnzKLcuth/OY/ayfVyWnOC58/cXLBawBEFAVTv/QXs5u2x2bL1O9dzuwArYt6RG8cIdmdw7dwOjEqN4fWYyIT6w8ddHWKBh99cmIAO9DK7RVKe82LD995wMCQ1LU3Ekr4R7Pt5ARJCNh84e0EwCNi/lThd2Wx1dQ0x/yM8wzGMmJeVObno3BadLcWlyQmX2MH9Dm4CqohWARlOdNXOgMBMmP9CgZgePFnH6s4vZdiiPh2cM9BuzR0Mpc7iqmG9qEDvQeM/aUVlktUjliP/euRv4bqN/ZAGrTphd5wV2RysAjcYdRyksfxG6j4fEiQ1qev+nGxDggxvHcElyQvPI1wKUOV11RyiNMWc2mdsqiwKsFj65ZRyf3zaeXtGhvPfbgWaWsnGcSAupw0GAXgPQaKqy7j3DvHHBKw1qlpFTzMp9x/jztH4eA6K1Jsqdqm4F0KEH2IKrzAAA+plpIzuG2inz0522NquF4AAr+SU6HAToGYBGcwJnOSx7AbomQ68pXjdTSvHgZxsJCrAwY1h888nXQgQHWDheVIbLVUtMaIsFYvpBljEDWL47m5cX7CLP7FQPHiuid4wPvZvqITTQRmGZNgGBFwpARIJEZJWIbBCRLSLymFmeJCK/ich6EUkREY+rZSKyX0Q2VZznVt5RROaLyC7zPcpTe42mxdj4MeQeNGz/DQj1sONIPkt3ZfPnaf3pXS13bmtkTM9O5BSVs+NIfu0nxQyEzO0cLSjlqjdX8uz8nazedwyAvJJywoP8cxEYDFdQHRHUwJsZQCkwVSk1HEgCpovIWOAp4DGlVBLwd/O4NqYopZKUUsluZQ8BC5RSfYEF5rFG4xucDlj6LMQPN3z/G8DiHVkAnDu8S3NI1uKM6WW4rq7ce7T2k0xPoOL845VFQQFWth/OIyjAStrxouYWs9GEBem8wBV4kxNYARXxawPMlzJfEWZ5JJDRwHufD5xmfp4DLAIebOA1NJqmYcvncGwvXPZegwO9LdmVRf+4cDpH+meSl4bSLSqEblHBrNhzlGsn9PR8kukJ1LFoX2XRVW+uBCDAKgzr1qG5xWw0oXadFKYCrxaBRcQKrAH6AK8opVaKyN3AjyLyDMZMYnwtzRXwk4go4HWl1CyzPE4pdQhAKXVIRGJrufdNwE0A3bt39+5baTQNweWCJc9A7CDof06DmhaVOVi97zjXjO/RTML5hmmD4vjfrwdIPVZEQseQmieYnkAhOTvpE5vI7swCrhidQNcOwfxhbA86hPivC2x4kI2MHJ0WErxcBFZKOU1TTzdgtIgMAW4F7lFKJQD3ALNraT5BKTUCOBu4XUQmNURApdQspVSyUio5JqZlk0do2gnbvoLsHTDpPmOBswFsTs+jzOmqkgO3LXDzpN5YRHhpwS7PJ7h5AvWOCaVXdChPXDSMO6b29evOH/QisDsN+rUrpXIwTDXTgWuAz82qTwCPi8BKqQzzPRP4wu28IyISD2C+Ny4AuUZzMlSM/jv1hUEXNLh5/86G6+OKPXXYy1shnSODuHZCIp+sSWPWkj01T3DzBOrRKZTU40U+SfHYGMJ0XuBK6jUBiUgMUK6UyhGRYOAM4N8YNv/JGAphKlBjqCAioYBFKZVvfj4T+KdZ/TWGEnnSfP/qpL+NRtNQdv4ARzbDBa81Kr5+RJANESN8QlvjvjP7k3qsiCd+2M6AzhFMqp6+MWYg7FvC5HExzFqyl7kpqVw9LtEnsjaEsEAbOcXl3Dt3PSF2K6F2GyF2G6GBVoIrj62EBtqqHIfYrdhtFgKsFqwWwWYRpBGJgfwJb9YA4oE55jqABZirlPpWRHKAF0XEBpRg2ulFpAvwplJqBhAHfGE+JBvwgVJqnnndJ4G5InI9cBC4pOm+lkbjBUrB4qcgKhGGNu7nJyKMSuzI8j3ZTSubH2C3WXj20uHseaWAuz5ax28Pn141ln9Mf9j4EeO72hjbqyNPz9vB6QPjKnMf+yun9o1h0Y4sVu49RnG5k8JSB6WOxilwm0WwWYUAiwWbVbBZLZVlVhEsImD8Q0TMdxCk0tegSrlbXUU5GDuYn780qcEJeuqVv74TlFIbgRrxcJVSy4CRHsozgBnm573A8FquexQ4vYHyajRNx+6f4dB6OPclIzRzI+nRMYRNabkopRo8IlRK8fnadL7akEFSQgfuOaOvX40qQ+w27juzPzf9bw3Pzd/JQ9MHnJDP9ASS7J08/fvhTH9hCX/5fBPvXtewAHotzcS+0fx4T9WlSIfTRVG5k+IyQyEUVbyXOykqdVJY5qCo1EG5U1HucuFwKhwuhcPpwuFSlDsryk7UOV0KBbiUAgUKhVLGuKPyM8YxVY4r2hmyHS0oZfnuHPYfLWp5BaDRtEkqRv+RCTD8ipO61MgeUXyyJo21B48zsof34Z+VUjz+3TZmL9tHdJidJTuzGNOzIxP6+FcoiWmD4rg0uRuvL95Lv9hwLh7ZzahwiwmUMHI0t0/tw1PzdrAvu5Ce0f67E9gTNquFCKuFCD/cwPbFujTu+XgD0WFNv7iuQ0Fo2if7FkPaKphwl5EO8SQICjDMIg0dub+4YBezl+3j2vGJLH1gKh1D7byzYv9JydIciAhPXjSMHp1CeOrH7Sc2eVV6Am0H4KJTumGzCI9/u7X2MBKaBnMkrxSA6PDAJr+2VgCa9snipyGsM5wy86QvtWr/MYIDrCQ1YPPTNxsyeOHnXfx+ZDf+ce4ggu1WLjqlKwu3Z1Jc5n/eNBaL8NofRlJU6uQvn2+qKDQ9gQwF0DkyiL/MGMiC7Zks3d321kR8xYGjhXQMtTfL7EQrAE3748AKOLDMGP0HnLxNtV9sGMXlTvZmF9Z53ub0XK57ZzVXv7WKv3y+iYHxETx50dDKmcPk/jE4XIqfth4+aZmag4HxEdxwai+W7spmeUUHb8YEquCK0UYY7M3puZ4uoWkEe7Oaz6SmFYCm/bHkaQiNgZHXNsnlpg8xIoD+vO1Iree4XIpnftrBL9szyS0qY3TPjrx0eRI2t7DLE/tEExcRyPyttV/H19w0qRe9Y0K55+P1fL42DVWRHaw4p8p5Fj9ayG7NOF2KrRl5laG2mxqtADTti7Q1sOcXGHcH2D2EOGgEnSODiA6zs+Ow5+iZuUXlPPjZRhbtyOKvMwby1R0TeevaUfSt9kctIkzoHc2KPUf91oYebLfynytHkJlfyr1zN3AkyIwVlL0TMDKD2a0WdhzO86GUbYedR/LJL3WQ3KN5dpprBaBpXyx5CoKjYNT1TXrZqQNi+XpDBnNTUqt03vuyC7nwv8v5fF06N03qxQ2n1hJczWRMr44cKyzj4DH/jaY5MD6CPrFhdO0QTFSPYUahmR0s0GbljEHGs9hXj0lMUz8pB4xoq8mJWgFoNCfHoQ2wcx6MvR0Cm3ZK/fdzBzOyRxQPfLqRG99NIT2nmNnL9nHuy8s4XlTGhzeO5eEZA+v1FLKasYjy/DxjVanDyaAuEQRG96ziCQTGDuJQu41LXvuVTWl6LeBkWLP/GDHhgXT3FJCvCdAKQNN+WPI0BEbA6Bub/NJhgTY+vHEs/zh3EAt3ZDLhyV/417dbGdI1gm/unMjont7tD5jQpxNWi/j1OkCpw0lpucvYwFTNEwigV0wYn902nuyCUm54dzVljdxlqzFmAMk9opptc6DeCKZpHxzZCtu+gUn3Q3CHZrmF1SL8cUJPhnSNZPX+Y5zaJ4ah3SIbdI34yGA6RwSx7mBOs8jYFHy48iCZ+aVcOz7RKDBjArkzx9zPULGz1n6Sey3aI0fySkg7XnziOTcDegagaR8sfQbsYTD2tma/1ajEjtx2Wp8Gd/4VhNitLNudzZ8+XEdOUVkTS3dyFJU5ePmX3Yzr1YkJfToZhR48gZbvzmZgfATLHppKVKju/BtDyv4K+7/3u8sbilYAmrZP9i7Y/Lmx8BvSfH9MTcWLl5/CZckJfL0hg4n/XuhX5qB5mw9ztLCMP53et0ZMoApPIIBh3TqQXVBKcEDDI6xq4M2le7n9g7UE2iwM7hJRf4NGohWApu2z9FmwBcG4O30tiVcM6hLBv38/jB/uOpVeMaHc/dE6jhf6x0xg9jIjBeRz83ewqyJpvFtMoArOHtKZrPxSZs5eyeFcnX2rIaTnFPPED8aayl1n9CXA2nzdtFYAmrbNsX2wcS4k/xHCWldGuYHxEfztd4MoLHOycp9/JJy5fFQCM4Z2ZldmATNeWsr3mw7ViAkEcPbQeJ67dDjrU3M48/nFfLuxoSnD2y+vLNyN1SIsf2gqt53Wp1nvpRWApm2z7Dmw2GD8n3wtSaOwmFaWYLt/+GvMHJfIf68ayYJ7JxMdFsiDn25EidTwBAK4aEQ3frhrEr1jw7jjg3V8uibNR1K3HrILSvlmfQZnDe7cInkVtALQtF1yUmH9hzBiJkTE+1qaRlGRgOXPczfw4xb/iRG0/2ghWfmljOnV0VgLqBYTqIKe0aF8fNM4hnaN5LXFe1DKP3c4+wtvLNlLYZmDu07v2yL3q1cBiEiQiKwSkQ0iskVEHjPLk0TkNxFZLyIpIlIjC4SIJIjIQhHZZra9y63uURFJN9uvF5EZTfvVNO2e5S8a7xPu9qkYJ8Og+AgemN6f6DA7N/9vDY98uemE7d2HPPnDdmLDA3n20iSjoJaYQGBkFrtyTHd2ZxawPrVmveYER/JKiI8Mpk9sWIvcz5sZQCkwVSk1HEgCpovIWOAp4DGlVBLwd/O4Og7gz0qpgcBY4HYRGeRW/7xSKsl8fX8S30OjqUr+YVj7LiRdAR0SfC1No7FYhNtO68NXd0zg2vGJfLDyIGe+sMSnNvX0nGJW7z/ONeMTiQw2QxR78ARy55xh8QQFWPhoVWoLSdn6KHe6+G3vsRZNplOvAlAGBeZhgPlS5qvCPykSI0l89baHlFJrzc/5wDagaxPIrdHUzfKXwOWAiff6WpImIdBm5dHzBrPy4TMY1aMj93y8/kRI5hYm3wxT0cXdRu3BE8idiKAALhmZwGdr03SMoFr4buMhDueVcN3ExBa7p1drACJiFZH1QCYwXym1ErgbeFpEUoFngL/Uc41EjNzCK92K7xCRjSLylog0T7QjTfujIAtS3oJhl0LHuoOvtTZiwgN54+pkekWHce3bqxj4t3lc985qjuS1nKtlQlQIFoFdmQUnCj14AlXnT6f3xW6z8P++29bu1wKW785m0lMLuXfuesBID/rG0r30jgnltH6xLSaHVwpAKeU0TT3dgNEiMgS4FbhHKZUA3APMrq29iIQBnwF3K6Uq4sS+CvTGMCsdAp6tpe1N5hpDSlZWlldfStPO+fU/4ChpM6P/6kSGBPD+jWM4tW8MxeVOftmeyV0frcPpIYR0SbmTY4VlTdrhhgbaGNA5gjUHjp0o9BATqDox4YH86fS+/LztCM/P30lBqYMF24741Ua35kYpxbu/7ueqN1dy8FgRGTnFAHy78RBbMvK4eXJvLJaWy6XQIN8ypVSOiCwCpgPXABWLup8Ab3pqIyIBGJ3/+0qpz92udcTtnDeAb2u55yxgFkBycnL7HjZo6qfoGKx+EwZfaHRIbZTosEDeunYUAHNTUnng043c/v5aHjp7AN07hvDpmjT+9tVmnC6Fw6UItVsZEB/BXaf3ZVi3SCKCAk6qo+neMYR5Ww5T5nBht5njSA8xgapzw8Se7Dicz0u/7OalX3YDEB5kY9OjZzValtbE3JRU/v7Vlspjp0tRXObkie+3MSg+gotHdGtReepVACISA5SbnX8wcAbwbwyb/2RgETAV2OWhrWDMDLYppZ6rVhevlDpkHl4IbD6J76HRGPz2KpQVwKT7fC1Ji3HJyG7kFZfzxA/bmbflMLHhgeSVlFPqcHHL5N5Eh9lJO17M/K1HuPqtVQB0iQzimvGJXD66+4mF3AZw4YiuzNtymK83ZPD7kWanFdMfNn5keALVEnDPZrXw3KXDuWxUAiv2HCVl/zHWHjzeyG/e+vhleyYAn906jj99uJ5OoYG8vmQPGbklPH9ZEtYWHP2DdzOAeGCOiFgxTEZzlVLfikgO8KKI2IAS4CYAEekCvKmUmgFMAGYCm8w1BICHTY+fp0QkCWMxeT9wc1N9KU07pSQXVr4OA34HcYN9LU2LISLccGovpg2K4/tNh/n3PMMM88/zB3P1uMTK8/4yYwDfbDjE8cIyftmeyRM/bOeVhbt569pRDQ44Nm1gHEO7RvLCzzs5d3i8sV/B3RMooYZXeBV5x/bqxNhenXjyh+2VSU/aOmnHi1i4PYuB8RGM6B5FTHggm9JzWbQzk3OGxTOmV6cWl6leBaCU2oixeFu9fBkw0kN5BjDD7RyPKk0pNbOhwmo0dbJyFpTmGiGf2yE9OoVy62m9mZuSyr7sQs4d1qVKfaDNWjlav3FSLzan5/KnD9dx9VurePOaZMb3jvb6XhaL8MD0/sycvYoPVh7kjxN6VvUEqkMBuBMeZKPM4aKozEGIn+x2bi5eWrCLMqeLWyb3QkSIDQ9kfWoOgTYLD88Y6BOZ9E5gTdugNB9+ewX6ngVdknwtjU/58rYJfHPHxHrDMA/pGslHN4+lW1Qw172zmi0ZDcveNbFPNON7d+I/v+ymoNThlSdQdSoyXbWHzGG5xYb77Jiexkg/NNBQeLdM7t0iYR88oRWApm2wejYUH4fJD/haEp8TGRLgdS6C2PAg3rthDKF2Gy8tqLGMVyciwgPTB3C0sIzZS/d55QlUnakDYrEILN3lmz0NLcn+7CIigmyVIbL/9rtBPHfpcG6b0ttnMmkFoGn9lBUZrp+9pkC3ZF9L0+qIDQ9iUr8Y1h3MabC7aFJCB84e0pnXFu8hPae41phAtREaaMMigqJtO/gdyi1mx5F8rh2fSGSIsejeMdTORSO6VcZ78gVaAWhaP2vnQGGWHv2fBCN6RJGZX8qerIL6T67GX88x7NePfr0FYgfUGhOoNlxKIZ6XCtsMecUOAF76ZTd//8p/HB61AtC0bspLjKBvPSZCj/G+lqbVctbgOKwW4bO16Q1u2y0qhFsm92b+1iNkBZk7r2uJCeQJxYmw122RNQeOc+5/llUepx4r8qE0VdEKQNO6Wf8e5B+Cye3T86epiA0PYlLfaL5Ym86KPdn8uudog8xBF480Qnz9mGVGdKklJpAn2npUiC/WpeFwuvjXBUO478x+PPK7QfU3aiHatt+Vpm3jKINlL0C3UdBzsq+lafVcmpzAre+v5co3jHBdN0/qxV+8dE8sdbgAyLbFNdgTKCjAQonZvjqHcourBI/rHxdOp7BAr6/ta5RSrD2QQ6+YMGaO7eFrcWqgFYCm9bLxI8hNhXOeA2nDNoQWYvqQztw5tQ/5JQ7ySxy8sXQvZw+NJymhQ71tn5u/k7BAG38Y1xP2NswTKDwooNacx1fPXlUl6FyAVXjp8lN46Zfd2G0Wnr1keIvFzm8MaceL2Xooj/vO9M+wJFoBaFonToeR7D0+CfpO87U0bQIR4c9n9geMkM8Ld2TyxpK9jOnVkZcW7CY6zM7ffjeICX1qbhjbm1XIiB5RRIcFehUTyJ2B8RFszsjzWNfB9Jj56Kax3PvxejJyS7j1/bWEBdooKHWw9uBxv1YAcRFBhNitpOe0XLTWhqDXADStk82fwvH9xq5fPfpvcsKDAjhnaDzfbTrE37/aQs/oEDLzS/nj26uZ8eJSvlxXdbF4UHwEm9JyjIMGegKN6dmR7Yfz2JxeczPYODM8wsgeUSx5YAqT+8UQFGDh/rMqFJWj0d+xOXlpwS56/uU7Jj+9kKIyJ1EhDY+31BJoBaBpfbicsOQZiB0M/XUm0ebitinGDtWpA2L54Max/O/60UwbFMfWQ3k8/eOOKuGnI4JtOCqOK0JCZO3w6j5XjelOdFgg932ygRvmrDbcSU0qI41iBJKbc91otv1zOleN6Q5AgR8qgKIyB8/N34lScCjXGPlP6hfjY6k8oxWApvWx9Us4usuI+GnRP+HmIj4ymGUPTmH2NckEWC0M7hLJK1eN4F8XDCE9p5h/fH3Cn91utVDqcBmeQ5UKwLt1gA4hdv7vwqFsP5zPz9syeWfFft5cuheAcmdNFyERwWa1EGK3VmYn8xcO55Zw+rOLa5RvO+TZxOVr9F+PpnXhchmj/+h+MOh8X0vT5hERpJqJbebYHlw5pjsfrkqtXLyNCrVT5nA1OibQtEFxXHiK4Uo6oHM4j3+3jVv+t4a3lu1jYHwENg8bBcKDbH5nArr1/TWVo353vljX8P0VLYFWAJrWxY7vIHMrnHofWHy3hb69c+Xo7jhdih+3HAagX5yxELs1I+9ETKAG7AUAeOKioXx26zi+uXMit57Wm5+3HUEEXrgsqYYSAioXgv2JUR7Cak/uF8O907QXkEZzcigFS56GqJ4w5GJfS9OuGdwlgh6dQnh/5UFG9oiib2w4AOtTc4y49g30BAIICrAysofRgT44fQB3Tu2DRYSggJqKvszhYk9WYdXE9H7AA2f158OVB8k3FVNcRCBzrvMuNLYv0DMATeth13w4tAFO/TNY9djFl4gI953Zn80ZuUx7fgkXvLIcixgKAGhUTCB31hw4zksLdvPApxtZte9YjfqNpseRw8MagS+xWS18dPPYyuP/XDnCh9LUj/4r0rQOlIIlT0Fkdxh+ua+l0QDnDu9CYqdQlu/J5skfDHt/Rcz7Kp5A3cc06LqvL97DEz9sJ8AqlDsVX2/I4IvbxnNK96jKc+IiggC44JQutV2mxfl56xGe/3knW8w9DT06hXg0CfkT9c4ARCRIRFaJyAYR2SIij5nlSSLym4isF5EUEfE4zxGR6SKyQ0R2i8hDbuUdRWS+iOwy36M8tddoANi7CNJWw8S7weqfPtXtkaHdIrnx1F70jgkFMDKDQYM9gSooKXfy/M87OX1ALOv/fibz7j6V7h1DuOjVFVz3zmqKygzTSnxkEFaLkHa8uMm+y8ny5LztlZ3/4xcM4b3rG6b4fIE3M4BSYKpSqkBEAoBlIvID8E/gMaXUDyIyA3gKOM29oZlH+BVgGpAGrBaRr5VSW4GHgAVKqSdNxfAQ8GBTfTFNG2PJ0xAeD0lX+VoSTTWsFuG7P51KVn4pCWaGr8Z4AgFsSs+lpNzFFaO7ExpoY0DnCD69dRzP/LiDuSlpLN99lGmD4rBZLcRHBrH9cH4zfKPaKSl3kp5TTOqxItKOF5uvIlKPF7M7s4BQu5WPbx7HkK7eJeTxNd7kBFZARTCOAPOlzFeEWR4JZHhoPhrYrZTaCyAiHwHnA1vN99PM8+YAi9AKQOOJ/cvhwHKY/iQEBPlaGo0HggKsJzp/aLQnkMtV06bfIdhemS/YPWbQ2UM6M3vZPg4eLaJ7p5Aa7RpDmcNFRk4xqceNDv5ER2908ln5pVXOD7AKXTsE0y0qhCtGJ3Dm4M6tpvMHL9cAzJH8GqAP8IpSaqWI3A38KCLPYJiSPAVj7wqkuh2nARXzojil1CEApdQhEYmt5d43ATcBdO/e3RtxNW2NJU9BaAyMuMbXkmgaQiM8gY4XGR38/qMnIoAeKyzjnRX7ARjV84RNfXK/WN5Yuo9V+4+dtAKYm5LKSwt2kZ5TXCU8tc0idOkQTLeoYKb0jyEhKoRuHY0Ov1tUMHHhQVhacTIDrxSAUsoJJIlIB+ALERmC0Snfo5T6TEQuBWYDZ1Rr6unJNGjZXik1C5gFkJyc7F9L/prmJ3W1Yf+f9k+wN80oT9NCxA4wIrYW50BwB6+adAw1Qj0P6BxRWZZdYIy6hyd0oGd0aGX5+ysPEBZoY+oAj2NHr1m4I5MHPt1Ij04h3Dm1LwlRRgef0DGYzhFB2Kxt11myQV5ASqkcEVkETAeuAe4yqz4B3vTQJA1IcDvuxglT0RERiTdH//FAZkNk0bQTljwFwR0h+XpfS6JpKI3wBAqxGz7/FYu9AP/3/TaiQgK4dXKvyrL8knLmbz3CHyck0jHU3mgRlVL88e3VADxw1gDOGRbf6Gu1RrzxAooxR/6ISDDGKH87RkdekYVjKrDLQ/PVQF8R6SkiduBy4Guz7msMJYL5/lUjv4OmrZKxDnb9BONug0D/DfmrqYVGeAKFBhpj0qIyZ2WZUnC8qJwOIXZSjxWRX1LOnBX7cbgU04ecXIdd4bXTLSqY0T3922WzOfBmBhAPzDHXASzAXKXUtyKSA7woIjagBNNOLyJdgDeVUjOUUg4RuQP4EbACbymlKkL9PQnMFZHrgYPAJU35xTRtgCXPQFAkjL7J15JoGkMjPIFCzRnA3R+v55ftmdgswq97jwJw+azfqpwbHxnEiO4dvL52Zl4J32w8xMq9R8kvcXBqv+jKjWTv3zCGmPDWk2msqfDGC2gjcIqH8mXASA/lGcAMt+Pvge89nHcUOL2B8mraC0e2wPZvYfKDhhLQtD4a4QkUFWpndM+OZOeXsjEtB4dLERxgpbjcWeW8/3fhEJISOniMEeSOUorP16bz+bo0ft1zFJeCntGhBAVYeWreiXDVgbb2GVdK7wTW+CdLngF7GIy5xdeSaE6GBnoCBVgtzL15XJUyp0ux/2ghm9NzefKH7cRHBnHVGO/y6z45bzuvL95Lj04h3DGlD+cldaGPGbfo4NEi5vy6n/AgG3ER7W/0D1oBaPyRrJ2w5Qtj129I+7PLtika4QlUHatF6B0TRu+YMB7/bhu9Y7xbD3pz6V5eX7yXP4ztzr/OH1JjttC9Uwh/+92gRsnUVtAKQON/LH0WbEEw7g5fS6I5WU4iJlB18kvKycovpVdMGOVOF1n5pRSUOigtd1HqcFLqMN/LXezNLuTpH3dw9pDOPHZezc5fY6AVgMa/OLYXNn1imH5CayYf17Qy3D2BTlIBHDhaBMCLC3by73n1LyyP69WJ5y9LwtqKN2o1N1oBaPyLpc+BxQYT/uRrSTRNQSNjAnmie6cQLhnZjQCbhdjwQGLDg4gMDiDQZiEwwEKgzYrdZjGObRZ6dArVnX89aAWg8R9yDsKGD2HkHyG8s6+l0TQFjYwJ5ImIoACevmR4EwilqaDt7nHWtD6WvQCIsfiraTvEDDTWADR+h1YAGv8gLwPW/Q+SroTIbr6WRtOUnGR2ME3zoRWAxj9Y/hK4nDDxHl9Lomlq3D2BNH6FVgAa31OQCWvehmGXQceevpZG09Q0MjuYpvnRCkDje1a8DM4yI9m7pu3RhJ5AmqZFKwCNbyk6Bqtnw+CLILqPr6XRNAdN6AmkaVq0AtD4lt/+C+WFMOk+X0uiaU60J5BfohWAxncU58DK12HgeRA70NfSaJoT7Qnkl+iNYBrfsf59KM2D1FXwysmFCdD4ORX2/zVva08vP0IrAI3v6DYKBl1AA9NEa1oj9lBIXwPBUb6WROOGVgAa35Ew2nhpNBqf4E1O4CARWSUiG0Rki4g8ZpZ/LCLrzdd+EVnvoW1/t3PWi0ieiNxt1j0qIuludTOqt9doNBpN8+HNDKAUmKqUKhCRAGCZiPyglLqs4gQReRbIrd5QKbUDSDLPsQLpwBdupzyvlHrmJOTXaDQaTSPxJiewAgrMwwDzVWm0FSPTwqXA1HoudTqwRyl1oHGiajQajaYp8coNVESspoknE5ivlFrpVn0qcEQptauey1wOfFit7A4R2Sgib4mIx9UhEblJRFJEJCUrK8sbcTUajUbjBV4pAKWUUymVBHQDRovIELfqK6jZsVdBROzAecAnbsWvAr0xTESHgGdrufcspVSyUio5JibGG3E1Go1G4wUN2gimlMoBFgHTAUTEBlwEfFxP07OBtUqpI27XOmIqFhfwBqDdQTQajaYF8cYLKEZEOpifg4EzgIqoTmcA25VSafVcpsYsQUTi3Q4vBDZ7KbNGo9FomgBvvIDigTmmF48FmKuU+tasq2HXF5EuwJtKqRnmcQgwDbi52nWfEpEkjAXl/R7qNRqNRtOMiOHk0zoQkSygtXsRRQPZvhbCj9DPoyb6mVRFP4+aNPSZ9FBK1VhEbVUKoC0gIilKqWRfy+Ev6OdRE/1MqqKfR02a6pnoaKAajUbTTtEKQKPRaNopWgG0PLN8LYCfoZ9HTfQzqYp+HjVpkmei1wA0Go2mnaJnABqNRtNO0QpAo9Fo2ik6IUwLICIfA/3Nww5AjlIqyYyR9DqQDLiAu5RSi3wiZAtTxzMJAN4ERmD8Pt9VSj3hGylbjjqex1XA/W6nDgNGKKXWt6yELU9tz8SsG4bxtxOB8bczSilV4gMxW4w6fiOJwDZgh1n3m1LqFm+uqRVAC1BH7oQbzfqhIhIL/CAio8z4SG2aOp7JJUCg+UxCgK0i8qFSar8PxGwxanseSqn3gffN8qHAV+2h84fan4kZg+w9YKZSaoOIdALKfSNly1FPDpY9FcqxIWgF0IJ4yJ0wCFgAoJTKFJEcjNnAKp8I6AM8PBMFhJp/5MFAGZDnI/FanHrya9Qbebct4uGZnAlsVEptAFBKHfWVbL6gATlY6kWvAbQs1XMnbADOFxGbiPQERgIJPpPON1R/Jp8ChRghwg8CzyiljvlKOB9QV36Ny2iHCoCaz6QfoETkRxFZKyIP+FA2X+DpN9JTRNaJyGIROdXbC+kZQBMhIj8DnT1U/VUp9ZX5ufoI7i1gIJCCEeNoBeBoTjlbkkY+k9GAE+gCRAFLReRnpdTeZhW2BWjk86hoOwYoUkq1qai5jXwmNmAiMAooAhaIyBql1IJmFbYFaOTzOAR0V0odFZGRwJciMlgpVe/MWSuAJkIpdUZd9W65E0a6tXEA97idswKoL7Naq6ExzwS4EpinlCoHMkVkOYZZrNUrgEY+jwo8ZdRr9TTymaQBi5VS2eY532M4DbR6BdDIfqQUI3c7Sqk1IrIHY5aUUt/9tAmo5aiRO0FEQkQk1Pw8DXAopbb6SkAf4CmfxEFgqhiEAmM5kX+ireMxv4aIWDAWxz/yiVS+xdMz+REYZv792IDJQHv5u/HUj8SY4foRkV5AX7wcMOkZQMvhaQQXC/woIi4gHZjZ4lL5Fk/P5BXgbYwEQQK8rZTa2NKC+YjaRvmTgLS2YAZrBDWeiVLquIg8B6zGcBr4Xin1nS+E8wGefiOTgH+KiAPDfHqLt+tmOhSERqPRtFO0CUij0WjaKVoBaDQaTTtFKwCNRqNpp2gFoNFoNO0UrQA0Go2mnaIVgEaj0bRTtALQaDSadsr/B/Zptor7sCVUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Now, repeat for southern central tip\n",
    "x,y = tractMAP.exterior.xy\n",
    "plt.plot(x,y)\n",
    "pt1 = Point(-76.2,37.8)\n",
    "pt2 = Point(-76.4,38.6)\n",
    "pt3 = Point(-77.4,38.62)\n",
    "pt4 = Point(-78, 37.8)\n",
    "clipPoly = Polygon([pt1,pt2,pt3,pt4])  #LEFTOVER FROM INDIANA. plan to clip off SW corner near Evansville\n",
    "xc,yc = clipPoly.exterior.xy\n",
    "plt.plot(xc,yc)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "2a56b3e5-803d-425d-8bb6-abfba9aa9716",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have flagged  142 tracts w pop 263227.0  to reassign to tracts...\n",
      "[19, 20, 22, 23, 29, 30, 344, 345, 458, 648, 651, 652, 688, 689, 690, 709, 710, 712, 713, 715, 717, 718, 732, 764, 767, 885, 887, 941, 942, 952, 964, 965, 986, 990, 992, 993, 994, 996, 1000, 1446, 1447, 1448, 1495, 1496, 2093, 2156, 2777, 2779, 2872, 2873, 2874, 2895, 3144, 3246, 3287, 3288, 3438, 3441, 4029]\n",
      "I have flagged  75 precincts to reassign to precincts...\n",
      "[297, 300, 340, 347, 348, 349, 368, 433, 436, 481, 526, 546, 757, 759, 760, 762, 773, 775, 776, 777, 778, 783, 784, 786, 788, 1669, 1671, 1672, 1676, 1677, 1678, 1680, 1689, 1700]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#find all tracts and precincts centered in this clipPoly, flag for cutting\n",
    "# and, ID all nearby tracts and precincts to receive their pops and voters\n",
    "cutTractList = [-99999]\n",
    "cutPrecinctList = [-99999]\n",
    "tractReceivers = [-88888]\n",
    "precinctReceivers = [-88888]\n",
    "popToCut = 0.\n",
    "for t in range(nTracts):\n",
    "    x = tractGeom[t].centroid.x\n",
    "    y = tractGeom[t].centroid.y\n",
    "    if tractGeom[t].intersects(clipPoly) :\n",
    "        CP = Point(x,y)\n",
    "        if CP.intersects(clipPoly):\n",
    "            isSkippedTract[t] = 1\n",
    "            popToCut += tractPop[t]\n",
    "            if cutTractList == [-99999]:\n",
    "                cutTractList = [t]\n",
    "            else:\n",
    "                cutTractList.append(t)\n",
    "            x,y = tractGeom[t].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        if tractGeom[t].distance(clipPoly) < 0.1 and tractGeom[t].centroid.x < -76.5:\n",
    "            if tractReceivers == [-88888]:\n",
    "                tractReceivers = [t]\n",
    "            else:\n",
    "                tractReceivers.append(t)\n",
    "xp,yp = clipPoly.exterior.xy\n",
    "plt.plot(xp,yp)\n",
    "\n",
    "for p in range(nPrecincts):\n",
    "    x = vtdGeom[p].centroid.x\n",
    "    y = vtdGeom[p].centroid.y\n",
    "    if vtdGeom[p].intersects(clipPoly) :\n",
    "        CP = Point(x,y)\n",
    "        if CP.intersects(clipPoly):\n",
    "            isSkippedPrecinct[p] = 1\n",
    "            if cutPrecinctList == [-99999]:\n",
    "                cutPrecinctList = [p]\n",
    "            else:\n",
    "                cutPrecinctList.append(p)\n",
    "    else:\n",
    "        if vtdGeom[p].distance(clipPoly) < 0.1 and vtdGeom[p].centroid.x < -76.5:\n",
    "            if precinctReceivers == [-88888]:\n",
    "                precinctReceivers = [p]\n",
    "            else:\n",
    "                precinctReceivers.append(p)\n",
    "print(\"I have flagged \",len(cutTractList),\"tracts w pop\",popToCut,\" to reassign to tracts...\")\n",
    "print(tractReceivers)\n",
    "print(\"I have flagged \",len(cutPrecinctList),\"precincts to reassign to precincts...\")\n",
    "print(precinctReceivers)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "264c0a29-0366-433f-ae45-63b986566b34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 2 of 4\n",
    "#visualize the to-be-cut precincts, just to confirm there are really xx precincts up there\n",
    "for p in range(nPrecincts):\n",
    "    if isSkippedPrecinct[p] ==1:\n",
    "        if notPolyVTD[p]==1:\n",
    "            for geom in vtdGeom[p].geoms:\n",
    "                x,y = geom.exterior.xy\n",
    "                plt.plot(x,y)\n",
    "        else:\n",
    "            x,y = vtdGeom[p].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "34f94200-4cc7-4226-9a6c-d6ae75b3380f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 3 of 4\n",
    "#now visualize the receivers\n",
    "for pp in range(len(precinctReceivers)):\n",
    "    p = precinctReceivers[pp]\n",
    "    if notPolyVTD[p]==1:\n",
    "        for geom in vtdGeom[p].geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        x,y = vtdGeom[p].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "9301c018-ac66-4a64-8efe-a839a349e53f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#now visualize the TRACT receivers\n",
    "for tt in range(len(tractReceivers)):\n",
    "    t = tractReceivers[tt]\n",
    "    if notPoly[t]==1:\n",
    "        for geom in tractGeom[t].geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        x,y = tractGeom[t].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e8d4b939-2d62-4d7a-a866-7283b3e8e383",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have finished slicing  263227.0 people (VAP of 201461.0 ) and  133760.0 voters out.\n"
     ]
    }
   ],
   "source": [
    "#now, cut and redistribute\n",
    "cutPop = 0.\n",
    "cutVAP = 0.\n",
    "cutHisp = 0.\n",
    "cutBlack = 0.\n",
    "cutTrump = 0.\n",
    "cutBiden = 0.\n",
    "\n",
    "for ct in range(len(cutTractList)):\n",
    "    t = cutTractList[ct]\n",
    "    cutPop += tractPop[t]  #sum up the cut pops\n",
    "    cutVAP += tractVAP[t]\n",
    "    cutHisp += tractHisp[t]\n",
    "    cutBlack += tractBlack[t]\n",
    "    # Now we zero out the pops in the cut tracts\n",
    "    tractPop[t] = 0\n",
    "    tractVAP[t] = 0\n",
    "    tractHisp[t] = 0\n",
    "    tractBlack[t] = 0\n",
    "# Now distribute the pops among the receivers\n",
    "ntR = len(tractReceivers)\n",
    "NTR = float(ntR)\n",
    "for rt in range(ntR) :\n",
    "    t = tractReceivers[rt]\n",
    "    tractPop[t] += cutPop/NTR\n",
    "    tractVAP[t] += cutVAP/NTR\n",
    "    tractHisp[t] += cutHisp/NTR\n",
    "    tractBlack[t] += cutBlack/NTR\n",
    "\n",
    "#             now do the same for the precincts\n",
    "for cp in range(len(cutPrecinctList)):\n",
    "    p = cutPrecinctList[cp]\n",
    "    cutTrump += vtdTrump[p]\n",
    "    cutBiden += vtdBiden[p]\n",
    "        # Now we zero out the pops in the cut precincts\n",
    "    vtdTrump[p] = 0\n",
    "    vtdBiden[p] = 0\n",
    "\n",
    "# Now distribute the pops among the receivers\n",
    "npR = len(precinctReceivers)\n",
    "NPR = float(npR)\n",
    "for rp in range(npR) :\n",
    "    p = precinctReceivers[rp]\n",
    "    vtdTrump[p] += cutTrump/NPR\n",
    "    vtdBiden[p] += cutBiden/NPR\n",
    "print(\"I have finished slicing \",cutPop,\"people (VAP of\",cutVAP,\") and \",cutBiden+cutTrump,\"voters out.\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "94a784a8-4400-45dd-a1ff-af76fce9e74d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "84c2cf09-b3ea-4fc2-a55b-62985882836b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on tract 200\n",
      "working on tract 400\n",
      "working on tract 600\n",
      "working on tract 800\n",
      "working on tract 1000\n",
      "working on tract 1200\n",
      "working on tract 1400\n",
      "working on tract 1600\n",
      "working on tract 1800\n",
      "working on tract 2000\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#rebuild tractMAP, excluding skipped / sliced tracts.  #NOT NEEDED FOR OHIO -- NO SLICED populous TRACTS\n",
    "counter = -1 #Watchout - low-number tracts may have been skipped\n",
    "found = \"no\"\n",
    "while found == \"no\":\n",
    "    counter +=1\n",
    "    if isSkippedTract[counter]==0:\n",
    "        starter = counter\n",
    "        found = \"yes\"\n",
    "\n",
    "tractMAP = tractGeom[starter]\n",
    "for t in range(starter+1,nTracts):\n",
    "    if t%200 == 0:\n",
    "        print(\"working on tract\",t)\n",
    "    if isSkippedTract[t] == 0:\n",
    "        tractMAP = tractMAP.union(tractGeom[t])\n",
    "\n",
    "plotPoly(tractMAP)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "6504beea-e316-496e-ad91-40afea33d501",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pt1 = Point(-74.33,39.45)\n",
    "pt2 = Point(-74.18,39.7)\n",
    "pt3 = Point(-74.5, 39.8)\n",
    "pt4 = Point(-74.5,39.45)\n",
    "healPoly = Polygon([pt1,pt2,pt3,pt4])\n",
    "tractMAP = tractMAP.union(healPoly)\n",
    "#tractMAP = trimmedMAP.union(healPoly2)\n",
    "plotPoly(tractMAP)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "650cf37d-e11a-4ed3-861b-3f3edc411422",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#What is our distribution of precinct's Trump votes by precinct Biden votes?\n",
    "fig, ax = plt.subplots()\n",
    "ax.set(xlabel=\"Biden votes in this precinct\", ylabel=\"Trump precinct votes\")\n",
    "plt.plot(vtdBiden, vtdTrump, marker='.',linestyle=\"none\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "d40408f3-31eb-4b8a-ad91-4e2eb5f422df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 0.5418250950570342 779.0264506498713\n",
      "100 0.48016492534326083 0.18903962522298579\n",
      "200 0.4540740740740741 843.1911652721938\n",
      "300 0.48312611012433393 661.6246222599514\n",
      "400 0.5955149501661129 13.582144293152957\n",
      "500 0.5196065743496829 556.2684039110947\n",
      "600 0.5051559356136821 609.3832167411975\n",
      "700 0.4031666344854219 439.4528235508814\n",
      "800 0.3702872273067403 0.2528146876587034\n",
      "900 0.39102752473307867 462.1062981580767\n",
      "1000 0.2098309934119439 0.004813769848862242\n",
      "1100 0.4444444444444444 0.10016271044093286\n",
      "1200 0.4747710890317553 992.5934888599045\n",
      "1300 0.3890926487003668 628.5345383882159\n",
      "1400 0.12027138976290998 0.003105576171928446\n",
      "1500 0.5171328247971909 654.8617933967744\n",
      "1600 0.1416483860858665 360.3825871916647\n",
      "1700 0.3138815207780725 0.2211661048101402\n",
      "1800 0.033106111669147155 0.0008406066442391425\n",
      "1900 0.4332606211696271 397.4027511034079\n",
      "2000 0.4917468063729008 0.8377534047988979\n",
      "2100 0.002925008250309313 0.00010896977487473777\n",
      "2200 0.14585584761491469 0.004249869845598996\n",
      "2300 0.3827487315243768 0.022712522394135296\n",
      "2400 0.49648755237860487 0.11163285962390611\n",
      "2500 0.4286882453151618 893.5513071831859\n",
      "2600 0.024968117323026593 0.0006853254034116225\n",
      "2700 0.5972803347280334 808.9233285934048\n",
      "2800 0.007728763111072351 0.00012811831033179234\n",
      "2900 0.0669383307613997 0.0019043042944071153\n",
      "3000 0.3312412831241283 702.1682143977126\n",
      "3100 0.2570156877595483 0.00904350582546722\n",
      "3200 0.2723435079888086 0.006129134694586401\n",
      "3300 0.3274273980639484 970.6210021582768\n",
      "3400 0.5813827468532351 0.009900839645491707\n",
      "3500 0.23776158738759612 0.006873449610249762\n",
      "3600 0.35956521739130437 0.5330877130515841\n",
      "3700 0.315136476426799 416.3206297390655\n",
      "3800 0.4612794612794613 0.6511561183513086\n",
      "3900 0.0 0\n",
      "4000 0.0 0\n",
      "4100 0.0 0\n",
      "4200 0.30785653287788217 551.7702401273215\n",
      "4300 0.7074340527577938 0.38242530337900965\n",
      "4400 0.40141304347826084 0.017705741166630956\n",
      "4500 0.19982158786797502 0.29947024875364797\n",
      "4600 0.05387496398732354 108.93701151203427\n",
      "4700 0.5076263107721639 0.018429230118720392\n",
      "4800 0.1743438671665774 1.527435184098081\n",
      "4900 0.05014925373134329 424.8658013643439\n",
      "5000 0.003907698471738649 0.0010013300993646547\n",
      "5100 0.14042303172737955 20.528673326847613\n",
      "5200 0.36787349146899706 0.029504614197621494\n",
      "5300 0.2035837651122625 624.1095349869154\n",
      "5400 0.04928031227128569 346.905150630521\n",
      "5500 0.5345293683951832 0.021850298923580112\n",
      "5600 0.05071209902835086 398.89378667262446\n",
      "5700 0.14596067737861543 0.0033450808866231996\n",
      "5800 0.1189873417721519 391.65733894532195\n",
      "5900 0.3910482921083628 0.013312756336350475\n",
      "6000 0.534389629096147 611.510334848022\n",
      "6100 0.17171408704000768 0.0032541759859948142\n",
      "6200 0.3429179978700745 680.9359481255894\n",
      "6300 0.619777013650099 0.015040938497168418\n",
      "6400 0.4858031368307193 11.645516740978271\n",
      "6500 0.32624237140366175 540.4856019142675\n",
      "6600 0.21089088253703314 366.2368447118044\n",
      "6700 0.19686581782566112 0.02480509894803393\n",
      "6800 0.256964656964657 376.7745266339433\n",
      "6900 0.2773839241138293 480.85266969231844\n",
      "7000 0.4103480311584364 261.19421781968794\n",
      "7100 0.38893946290396 815.6321833136464\n",
      "7200 0.1590468589670505 505.1837590288612\n",
      "7300 0.5617896445967887 0.17481304466610662\n",
      "7400 0.5884751223429945 297.28038559000794\n",
      "7500 0.0974404236540159 276.86170132975315\n",
      "7600 0.22454736289687743 0.21034495638157896\n",
      "7700 0.4454074578509668 0.43029637016509015\n",
      "7800 0.14314618108244814 915.1612894211132\n",
      "7900 0.21618594231307758 223.8702769611983\n",
      "8000 0.3046270307051119 1067.9554251800198\n",
      "8100 0.30403577540992655 1.429850367507903\n",
      "8200 0.5829134218964728 0.01451308278880173\n",
      "8300 0.17389423596842216 0.013605671251821879\n",
      "8400 0.4212133459068661 640.3027535151446\n",
      "8500 0.4846686449060337 519.4327614115376\n",
      "8600 0.2997294435948712 0.033517626859260075\n",
      "8700 0.5587076438140267 0.017662495308826957\n",
      "8800 0.2778239202657807 2182.363844064367\n",
      "8900 0.37773209517733514 453.44499099262293\n",
      "9000 0.3777320951773351 423.34698517493973\n",
      "9100 0.5934968625213919 1.6289196431772495\n",
      "9200 0.5417812368923894 1191.7027254531731\n",
      "9300 0.3550762329795786 0.00837169285634328\n",
      "9400 0.4625463535228677 3.2457625369585945\n",
      "9500 0.5725532212232729 1164.948593640006\n",
      "9600 0.0 0.0\n",
      "9700 0.5939380911435941 1512.706688792374\n",
      "9800 0.46001362819798053 845.8300761670957\n",
      "9900 0.16589706297750703 0.0027732323585771086\n",
      "10000 0.32991913746630724 0.010054772401025612\n",
      "10100 0.289470734695268 0.007727443192291293\n",
      "10200 0.5447732139380962 564.8757901682811\n",
      "10300 0.06349469212999745 0.0011817528633633807\n",
      "10400 0.6655199788303784 0.02765117776170139\n",
      "10500 0.5190725504861631 627.5149911775136\n",
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fzeVh368+pGBpAQMa3CS/sBcPcHJwIqn35XFmxADKnY2s32PU7FcVwZjMZD/x9wVtf2emnUeATwFfI87dwJeBJ9rawTs5/Am4CagBPhFCvC6lrOzacGO4HJAQp/LSd+zc9sctPPK3cp6/76pWSptwAu4MMXZ2krDarSHH7+x5I4lwjuw6wnPf28Ew/RAD4j5gzuOGddlVV1RPodW9weEPLBfr1+JGoMnOxZidJU4qiioAgySDrysaqzzShLhq5irDty8BhXY/R7sdspfaePZ9Bd3jCwZAfWk9ugQBCAQV5JNLBVZZAxaLf//SJ0t563tvIXWJGqeSlZ9F+cpy/6SBDofeO0T15mquevgqhCIwaZLBJ1zMHpJO06Gz/LBgFImTMqioafQHe9+oqOPFj6sBSFBURikwtn8Cw9w6BdcPZ1LB8I5vbg8hKqIXQmQDtwL/A/wAQEr5qfe99nadDuyXUh70bvs34A4gRvRfcFyRmczP7pjIo6/s5LfvfsaPbhnbaptwAm4PJSVQVGT8XljYziQRhUY9+LzRWOHhIpyiInjnqWb+WduNikaLG3Y8s6NVLMBR7OCsxUZ5g/WCufrDJ9A0amDV4+B2U6BuxSwkbk/0MWZniZNVBav8SpfyleUs3LjQf6+CVxCeZo9/QgheQYVPBBVFFYaCxosh04Ywe/nsDieI/PvyKf1Lqf/985qZw2QwDCcg0BFUYSObWk6VHSDNe4y3vvcWUjNY3bcS8E8YXkjdGP+Hv/4QKSWKqnDVw1e1mry/ZLfypbGGYmzrh5LZXzmLsDQSn3UC85CD7BnkQSqC36yvZOjm/Uy7YqBf4jnckkRSvNorwd5oLfrlwKNA+xWlWmMo4Az6uwa4KtKGQohFwCKAnJycTp4mhksRX5mazUcHGvjTxgOUVTfyo1vGkB+ptnwHKCmBmTPBZVRRYMUKKC62MmOJtfWGndC/R+vrDxfhAGTr1ahoKEh0IGVICkd3HUVzayiqQvnKcrQWnRZd5TmlkJ/HWy9YkczQCdTqDyzbCwrYgNopH72j2BFQ0dDaxWIrsCEUYRCphLJnyihfUY6uGYHY3IW5rdxsZw+fDTlH1pSsVq4vwCvb1EBVGfcvs0nEsPw1j6HyicflJXnDOFeRZFONBxPvHx7PZ8sgrbrBT/I+xCXE+Vc9KAotLaCgIST4iu5IXVJfXh8y9jeLGmgsDkzamz4QnD+SjFaXzNldQ7iVMrLjnDRkJnI8uz9yli2knAOAfYSFvy66OroPshPokOiFEPOAo1LKUiFEQSePH8ncj5iKK6V8EngSjMzYTp4nhksQQgj++86JDE3vx4sfV3Pnnz/kxnEZfH/WFUzOTov6OD6L2oeWyC7YTuvfHcUOg0h0icfVtq8/XIQDcNcKG5pbRaJhilO45tFruObRa3AUOzhVfYodT+0A3ahKmaM7qHVbL54cPyiwbKdzY7AV2FDjVL9FH8nFIoOks8GuFY/LQ/2OehRVQccg/kRLIvvX7fdvr5gUcgtzW026uQtz/Z+NpnvY/dhbqEKimBSGTBtC3bY6hHEaBIa68xDDeIpFHBZD+Ou6u2h5A+JMk3lIvEd/ec5/zjMkwsJCRuLAgY3XnzjKrfItQqhLQFxiHIrJGHutmsOylZNp8QRsCL8B4JIouk6SOI/aIsmsPceQY83c/du5WO1Wf7euR/5WTlJ872TrRmPRXwvcLoSYCyQAKUKI56WU34pi3xog+MnIBura2DaGLyASzSb+9eYxfPeGkfzktd38Y0ct7316lO0/vtGfVNURfA+Uz6KPi2vD7dBJ/ftZi40WXUVBQ9dVzlpsbW4bLsJ5rtjK+0WF2HBwfWHA9wyQlZ/llRwax61WbJesHN9qt7KweGGbPnpHsYPgMitCESiq4re667fXo8QpTLl/il/37p8MBOTfl4/VbmXzss0h1jMAqoqmG78r3oir3qJz+qQeYk3qgIaJd7mZI+pQvnpbEy1vKGiaseepsVfR/9P3/dsu33oVVVutqKqVuXNBKg2gyYDVKozr2PfGPhTVGPs5rqPlKeOYLhcsXWr827ABiooEZw43k8gsnn8rgWxPFbXSxo1YsQJZqf2orDsN4C/k1tPokOillEuAJQBei/6HUZI8wCfAFUKI4UAt8HXgG10aaQyXNc65PbxZYaSn/8uNoxmQGH3dcbsdNm4M9tG3YZV2Uv9e3mDlOaWQHN1BtWIjqcHKnE6MyW634rNzwi3Sqx6+ivryepS8yXw37cL56HsDVruVGowViYVQy85WYMMUbzIkjkJwzb9ew5j5YyheWsyh9w4hdYnu0UnNSfVPED7dvaIoZOVn+Y8THETOLcxFzc/lx99rYLBWy5UERH+nPz/sDcAaNvghMYKh3yjggQlWLBYoK0tG9RrOZjPMXTya3Q9/gNaisVvJo0rmoOnGwu+11yQmMYl8pQyrrEZRFUbPG81nb3xmTDq6xt7DaZwenIbJZJTc1nV4713YvFmwfDmsWgVudzKQjK7DQZmDqgUWlFv3H+eJTQf55+k5/lr9PY0uiz2FEHcCjwODgLeEEOVSyluEEEMwZJRzpZQeIcRDwNsY8soVUso9PTLyGC4r9I83kZxg4kSTm29fZ+u0FC1qWXsUG/p8wXkWozRwrdvaLYs7XCrocXko+V2JofLYXE3hhgEXVIHT0zLP8NDH6uVO+jcEjj97+WzWPrQWvUXnw19/CEDB0gKqN1ejuTSEIki0JAKEbC81yfrF6/2yzPAAew2wS7FyVLOQTzlxwoOOQoWcyEBOkI2R5FQpx7Pv70eZPbqEX35+NVV6DiYT3H+/zyjIZOqkhTiKHeRaRrNusaC52SBtKQUeCYeEjcl3DOPaOanUl9X74w5Omc2qNVehCx2TCbJkPbVkoUsFt0uyerVgkMtJju6giUQSacKBjaOqMbmfPOfmBy+VM2JQEj+ZN67bn0Vb6BTRSymLgWLv768Cr0bYpg6YG/T3WmBtdwYZw+WPRLOJJ+6aylefKOE/X9vD776Wd1HGEW55r15e2C1VjP94Lm8ZAAUURUFq0igH0M0SAJ0Zhy9pqadlnsGhj0EuJ9seKkLogeM3NTT53TFSl2x9bCvpI9MNQn9wLbqmhxB6U0MTUm99f3z/Skrgfx+A9euNeEwNVlZRiD3TwcdHhlFNNgK4hg/5EhsYyeeM83wGlfDPfM4qCqnHSk5O4DP1HdtZ4uQ3C8vZengkL72eiEcHFZ1h8hD/eP0Kdr+1k6GeahCCciZTQzYaKlIqtLRIkjmNiQw0DFfPlEF1XKkXcZwBZHAcgURD5fTcQux2Kw+9uJsT59w8s/DKXk2y6lvpWzF8oTHNNoDvz7qC5e99zvWjB3Jnfna3jtcVyzU8oah/g4MlQeqdzlYQ9h9PlwhFMPzG4YxfMD6EbDuTDNaV6wuevIQiDNKVoIUFmIMng21lRker+YVpHV5ncOhjpHCAR0NK6T++rcCGECLEV1+5uhJbgc14LSwh6qzFhlRUkJo/QOurU1ODlRtuMAg+GDVYeflw4B5IJFu5lhROMdQbFhQY6pnhHOK4aSgFBaEyxuACbGMUhVtHzqfx8+MM5xBDqOdd/UbO6Qno6BySNgQ6ZeRjOIgkEviMK7iVdZylP5/K8Xz8dwcjyOJzRpLJByhIJBrmOgd/XhPPmzvr+cFNo5k4tHfLdMeIPoY+hYdmjmLr/uP8ZM0epuYMIMe7pI8GwSScTddq5bSXbNWWOnPNk0dYt7qJOQsSmb8os93jjV8wnqaGJmYvn+0vB9AVizqS9PPorqNUrq5k/ILxTF001b9t8OQlpfSLR6QucTW62LxsM4mWRNZ9fx2aW0OXglXcRTUp/GGlzj/+UBviigkeg4/IN2wwfPQ5jSnsfywgQfS5ZHKuy6FqU5V/X3OimURLov/e1Ko5vFk9iU+fhMWLrQzSChmpOPjuw6ErkCO3FJLWYuYYg8DviY8Ew0NfarmZvAkfwaYA2Vto4Lf37CQbC5uXBUoznKo+5XevVelDqPm8mcOMwIOJd7mJGrJR0VjJ3egIVHTicNFMov+cEpWPk2aiNJ3jiMwkXnejKB4O6cOZwRYkGjoqL3+ehdxYTk5Gf75zQ+83RYkRfQx9CiZV4Xdfy2PO7zfzLy+V8/J37FH568NJ+DcLG7pUK6e9jNw1RY24mlPRpcDtkjy71MHBvNPc89h4PAxi5TsaL3EkhOzDa7101m0STKa+gGdBAXjCVh5bH9vKZ2s+A+DgOwcB/GQfPNkIRaDrgWQgX6wACNKTSyayBys1nHf1Y9tDb4e4YnwujvCJZskSK5uXnWa/gt9NVV9W779mJU5hwKgBnNh/gs/e+Iz9b+9n9vLZbCtTDWniUwoKOhO0nRzHwiYxgy+Vb0YPus6EzyqYQBLF3EDbJO+DQNME4xIcHPS+oiDJYyeZZyTPXv8GumasboQiqFGtHJTX0Uw8JdiRCAQ6NWQjUDCpkmotB4liHBuBFkTyvp/HzvUH+iMEHIu38sjDZrb8biDPee4iR1bhiM9GX1CFIuE6OY14U+83QIkRfQx9Dtnpifz0tgn88OUKVu+o4SvTOibocIm8AxspYZZ5tG6XSBm5zhInjSs2oMhvIlEQuo7+3kb2vVfHYNJxkoMHybrVTcxfFPl44RLBjiYfZ4mT/y7YwIEWK/3VnbytDqXFo2A2w4vLR6OaN/mv70zdmZB9K1dXhlj1vjouTSlZ7HxsPQoaIMAjjSSgIM4UQD7lKOiGp9mjI8NKCkeqJwRwqvoUapzqr0wJgSJxACnWFBr2Nfj3a2poojFnBm63RJegIVFpYRT7kcLM1AU2dmwOfI5jxsDxTw+xmRloQeQamp7je10y7vRHHHz3IOHY9cIu4x6TjQMb/fQm1uuz8aASTNoSQRonsY1PwjY6ntdekyCDz9f+ZPOfD5/gyrR9/O2P8H+rc3h3QzaD5n9CQloTDa9czR0vJrW7f08hRvQx9El8OX8oL35cxS/X7+WWiYNJSWi/AUS4RH5+YRrZhQFLelORg5+uAIdm7VLBLkexg6FaNQspwoENGw6y9RpQBCM4RA1DMaEzZ0HbrqbO1uB5s6iBZ9zfQkMFj0R4BBLjGj9tyAxZeRzddZS6bYEUlay8LL9LJngVwcJcv2T0vEhknrIeoWtG6WAp0TUdIcCk+7JdjdgChCZDhV9LoiXRb+H7tOW5hcbkElwkbvyC8YbaJugebF1zAl2m4ytsU0Y+EoU4KUmbpLYqOvfRG++Rr5VxhiSSaOIYA2kknTOk4CP9fvE6N6d/wpDDO3mTuZwlif6cI5edRrVODJJfRSEaKgLJEGqxUYWDYdSQg4/MG0mnvFKwcy9IP8lLVFWg66CqkJ0N9fWBXA4wVDvP/PokB/gcW/wm7v3tPVT0P0yC7Tgn10/mt0sGXDBJbYzoY+hVdFXKpyiC/7p9Irf/aQt/eO9zfjxvfLvbR5bIG+crmlWEp1nj61JlFYXUNQ9lTdFp7Pa0qMfjI7Ycdy02td5bT12gmlW+/mWVjI93MufL/Zi/qHXNHh86W6htQ+UQg+S9VqOR5akTZ4KCAiVk5eH7Wbm6kqy8LD5+/GPDVSNEiIJlJA6Oxc+g1m1FVeGkzCBbd+CSidw/r57MwUZC17rvr/PXmxGKIP/e/JBkqPBrCbbwdUJ18eHXnDEpI+TvQ0sPIUjzukQkOgqgoElJcTEsWRK4ziefhGdlIRoCFY27eI4W4jhJGm8xDx85z3KtJfdwGRoqVVg5huFOK2UqY9jLtZTgwGYoZlDIpppCnkNFQ0NlFXdRg5VsarDhYDcTaNTTCcQFDJKXEjweqK6O/Bnu14fjIIevx73Ae84K+l2hc1XCOB75P+sFzZuIEX0MvYbuNg+ZODSFyUNTeXrLIb4+PYdRGa07VAUjkkTe3+FISr/i4rAcTOOKNTgLZ0U9nkjljIP97hPdGnWPqzjnt32NhuvISkGBFWsUD7mn2RP0l5HIP4KD/Os9Z7Hb81ptP3XRVKYumhriIpKKUYALYZQTSOUUq5c72VEGJ3c4eHm7jYPSxkJPETWvaRxOMJKR8u7Jo/SJUqPEry5DiDv4ngS/pppVNJcHVZHYLGfb3C644NnRXUe5IvE8KtloSCPDFcM7YlIl4yzHwUvSJSXw4IPg0X0+ckmVGI4tN4WpO9cjdPiUcYzjU6ayw2vbayRynuDJci/j+JzRzGEdOVSRg5M0GjGhIbyqGBtVnCORhRSholHLEBoJrcMUJCIiKOwRBgU1q5EPvzwIM/D0BW6T6UOM6GPoNXSleYiuS8qcJ1m/+zDr9xzGeeI8AEdPN3dI9JFgK7ChmBQ0XUMVAovewEKKGKrVdlq/HomwovG7+yprrlhhxBCidR3dfa/KG9sMlQYYeu4vxW3GyiDWPJnFpw2ZEeMN4W6V2ctnU19WT/mKckqfLEWIHQhFkKxJ7tJVKshFxZgMfdeQW5jbqbr8VruVwuVTOPS9x7C1HGTo938Bk95vNThfSWOjqJvm7zp1h3qcHXouTbIfqgrj+RSbdog9i+uZOsmYPIuKDOvZ554RwC45ng92ZfKjf85g2rpXmXpiBwHnCugoNJNAuE9dQ+E8iSzkOS+5g1Dw+t9VsmQdQ6n1F6YbTyWHGEGgek7w8SLFB4zfE8fWMvDWnaQlxPPXh69idGZn60L2DGJEH0OvIRqftKZL6hrPs//oWTbsPcI7e45w9IyLOFVw3aiBPDRzFDeOy8QSZd2biPA+h4oKU017/JUTo9Wvt+d+6ugafWogI9PSkH2OaHbwfpHNWyKhbcxflMlqjrDyGY24hDiuHvAZzWtree1JySp9ALoiMccLNmwwtg+4rYzs0mCp5eZlm/31ZSTSHxyNUzSmTwMqVKQncA1d6QeQtu4FrtOKUQDNJWgsWkNaENH7derehh4+SF3SKJKpwoaOYIa2iWvZjEDS0izYVORgBFaeeir4bAId1XDJaPCLF6xM5Vrm8ab/0A6GYcXJ1/k7pUyhjHzOYRgLKjo2HP4VhADQQTEJWq4Yx7hPd3OMgV7XjkYeOzknUtg/+BoaTiqcb1YIJ/XA9GL8nTimnoG3lWNNTOf1H01jQFL0ZT16GjGij6HX4BqZRs5zd1C56zCurP4sd55g6Kf7EFVV7B2QzUEtnqoTTbi9WZP94lQKxgxi9sTBzByb0WEANho4ih1+CZ3UJfn355Oakxo1eXXkfuqIEH1qIB/JL6QIVWroK1WchR27suYvyvSreDYv28vGN3QO6cPwoCJ14a9/b9RTwavIOcIebwC2enM1GZMyDHmmsHKIYdhwGAFJAaZ4lcLluUBuq2sIdrEE/x0RJSUkvfFSEN0pOLBhCZokHcUOw+8fXptWgZEmJ1ukxN2iU0UOHrzF5KTKfz5jI7sSbxGytiApZSogGcenVDKOLOqxUUU6p5jFRsawjwq8XaSCgrIhR5GSjDFpHP1UZSDHOUImLWmDSLGlM/3qXP63MI7yonIe+sskbywhcP5gtU+/Kw4z8LYyrP3SeXvJdJLiLy7Vxog+hh6FlJK1uw6zYushSqtO+l9PrtNIPnySw+c8SDGI7D01DEoewJfHZZE7OQubJYk8axr9zD2rKY5UDKutpJ9IRBaN+6m9BinBaqBRODDpGkJKpKfzpQ981zLcVYVJ19AVgdlskEuwtHTd6iaGBJVX3lTkYAszKBJ30wKoaNwT9zy33zuoVYA1+L74XCw+qWTIJBemVa0qKiZbN6hPB9aJuRxPmUld0CQ5e/lshCr8un2ftPGaaW5+vHwieUU7Wf1EA6dkCqu4y6uAsVHTkk3dtkh3JFziaJC9QfiCW3nDv6UArNRipdb/WmJGIgPHDiRxQCKfr/3cv9K789Er2D3nCkpXO5iVl0jL6SZ+usKGY1cay1fAzKtHe509rccj4jTSCz4leUo1qZ5U1i658qKTPMSIPoYexu7a0zz44g4AfnzrOOwjLWSnJ5LaLw6WLaN52c9xaoN5yfMNNOFETdjDdRsKsY60dHDkrqE9izuaYHFIspFJZVO1DVNJ9NLMYDVQnsXGjsVd64Mbfi23Wk74ffQQatGPyUvkxDuB8sr/+YyNQx6Q0rBAdQHp997JvP9Li3ieSC6W4K5Yoy0NZC7+hv+Eu5Zv4OEVBayVZuJw04KZZfLf2f6b0SyUWWTrTjS3xrYylSO3fhvX628jdemXNm6tgOxdCg6SGRlXzh73KGowKmL60Nzc3p0xvPHgc6cYf+9mItPY0eZe4748zl8aedScUTQ1NJFoScRR7GBigY2Jk2ytFFs1mpW3NyUSbsGDID67AcvcnZjSmjizfTirlo4hJaH3k6GiQYzoY+hRjB+SwrzJWby5sx6bJYkJQ4JqeBQUkPDzn3PYbfUWgmq76XNPoi2LO1prvXBDoVeHb8PxlJX/WdU6mNpeMlZADWRl4qSu9cGNdC3zg14PlpYWF2f6tfJV2KjxWEMUIqY4hdOkUdLGhBVQKnlf8Cp2fJ2hPGILGboboRtLiIbVxWzRljCLDcykmI0U8BF20CQVai5WtSaoMYdCXNzdzBxRjb7XKAbmboGHHgJdT0NVCvH445vBRBqOwOBycFCDFd3/usYUypjEHk6SxgAaW+2txhu9YYvCVhzBOQe+7lc+xdYAGoImnyD/vKqTNmMfKdMPop9OZLTTzs9+fuE08tEgRvQx9BhcHo0Nnx6ltvE8qiJaF2rymre2ovdRV+poHhm1ZdvTpXUh+gQmq91KdbEVhxa5OVVnOhQGV0n0FerqievxTSbOEic51Q7iTDZKtBmoKphkoAiYYuRF8dRTxiog0ljDSyYMzh9M8pBk9r2xD6lJDinDuF41oQrAbMayoADzZvjEbecT7EG+dMHo20Yzc3oTb1ZP8jfmAIVhN9iIdxgJRjJofJoW3i812HIORuC1RtLQvdr2bKqxUUUSZziOhVOkhhC9Eqf4cwLCJ/rK1ZWtmpsIk4quayBUHLqt1SjMmaewzCvHPPAsZ8pyeOXH45g5o+/Rat8bUQyXHE6db+HHa3bzXuURzrdoZKUm8LM7JjA4NaH1xnY7VrudwsLoibu7evy20BllSXvNqSJ1KMym7evrresJPu5Ck4pyfyFfKjRkiU88YRCqL8FHykAj8/CVSA1WWFhI5uHdHFtbSn1pPUcqjvhb/tWbbRxf/jcyGz6FggIm2e1smGQcx2KBhx82iDsuDh58NBmYwekiQpp9FBZCSgr8+tehevTWaK/EgLHjadIAnUEcJpEmDjKc4TiIx0UGRiG1VFsqo2aPColJHN111EgoU2TErF1HSi5PuXIZhgOHtHnP44Wik2rfT+o1+9HOxXPkpeksuGYQM2d08kO7QIgRfQzdRoWzkTcqjPT7Wydn8buv5mE2te5kH26Vd7V0cE+6eqIdR3vNqcIngTxL+0Te1evpqFZP8HFB4/och1/C6fPhqyoIYZC9yQTPPGP8bjYbXbrAtzqxojCUXG0guewkh1qm3D/Fr1jKtFuB+caYlhljWrLE2H/SpND+ub7VjskEt90GgwfDrl3w29+2l2gUDYInAYXjZPgzYOsYCkhMaNytPMe3X7ypVXxm/eL1SN1IKJu9fDZTF00lY1IGbxY18GLJSLY8ZmjenYR+Nqb0cwy8fQfxg0+jHxyK2D6B738ljl/+sjvX0ruIEX0M3cb1owfx90VX87/r9vLWzno+P3KGR28Zy6xxGQhhPIzdsWI7WyOmt9BWc6rwSSC8smQ4kXfleqJxD7V13EjNy4uLYds2WLPG+NvlMqz7nJzA6kRDsJ2plJPHt9UXuCdMsdTWmILv07JlgeNJCW+8YZC7onQkl+w8ZEjZYqNujoYk/vZbsNqHhmwb3CcAAU0NTQCs22XloSet7UxAkoHzyjGlnmfs8amsf2lwz15ELyFG9DH0CK4aYWHN965h3e7D/Ortz7ivaDtTctL44S1juGbkwG5Z5V1J3ulsg5DuIpjcnLRP5F25nkjuId/rvmts77jhk5TdDg880Po8vtXJwPNOw2WBjToxlLRvz8caVhuouNiYIHQdzp83Jorwex282oEAufc0yRsIzVIVSOLjBd98dGirLcMnxbMWGzfcAJs2tX+GeOsJ4oc0MvLkRNY/fWmQPMSIPoYehBCCuZOyuGl8Jq+U1vD79z7nG099zLWjLMxLMhlVECWdssqD3T0zlkTnAO1McLQz6EyZ446IvDOuK1+A1aYaqU5ms+ELj3SNnTluYaFRlsHnT/c1VV+93MnH3ytCah4kCquVr3Casa1UOhZLqOvl6adDG7P77tfDD0N5OVRWQk3rHKUeRsCdM+N6+MUvRMTPymq3MmV5IRuecVCfYOOn37NGNfmkXn0A7ZyZgmHd6352oREj+hh6HHGqwj9Pz+HO/KG88HE1j7/zGVvdGtbbRzBtaz3f+q+ZPZKV2hYiWb/dJfrOTB49qRBqK8DaE9dotxv7hU9e/RscCOlBAE6GsE8byWdPSlauFNxzT4DMy8pCj+fxBIK7FgssXhyw+C8G6uravif/9m/wq19ZkTL6zydu0Gn6jTjGqc1juPHXfUMfHy1iRB9DryEhTuXe64aT9sIe/uxs4MDEgThHpUHtCaZG2D6cILvq7mlPIdNVREusnZmcolkhtBdg7e41Rjp/SQm8WT2JE+JzQznkK+WrCzSXod7xyTINBIp8KYpg5UqD8BUloO65WPjylyO//m//Bo891vnjJedXobtVln5rWJ/SyEeDGNHH0KM4crqZd/Ycptx5ivpT52k43kTD+SaOTxzo32b4mIGt9otEkJ0JWoaT1urlTkpXO5i6oOPiYdEg2skj2skp2hVCtAHWzhLPuied/OpBBwd0Gz+Pt/qJ2xhTGiblbu7iWYbrVZikhkcIpBR+WWZxMdySf4SnsKChoKIzc9IJ3t85CN27XbQkr6r4a7t3FkLAj34EI0fC6tXGawcPGiT/y18GKoeCIed8803DhRTtsYUwxtZ/cjX9JzuZkJLFIw90vwbThUaM6GPoMs40t7DdcZLPj56h/lQzWz4/zv5jZ5ESBvY3Y0mKR551o2iSaRudjKxsYMa3cpn3jfxWx4pEkDOWzIgqaBlOmkZRryJ0t8aOzSoTJ3Vfpx4tsUY7OXXG9eJrAxhep6ctFVBHKH2ylG0PrGWGLrkGledchRQXG8f1jQkUzt/2Fc7UnWFmgpvEAXGsXRsos1xQAAeL9nE3n+NgGMNFFewGRd5lVJWXgeqOimK4co4d840gtEaNfeIZ4vaUc0C3Ua1H9zmpKtx/f8CNVFICDQ2tVycFBYFAcDSYPx/mzAkcy6Pr/PiVSqriq5g0aCDPPzAx+oP1IcSIPoaooemScudJ9h05y5b9x1m/+zCat0CV2aRw9QgLcyZlcXvuEEYOSkII0cpS97WXC0dbBBlNcDGcNI2iXh7QwePytOvy6Yw/PRpijVZRE80KIdp71xk4S5ysfXAt6L7aixrDhYPqaiv5+YExqSq89FYyLS2GltxshscfDxAgwE9X2Pg6mxhCDaoQIHUWUkQ5uZTKfPDV0VfhxAnAW6RAoCNR8RG+s6KBu9lIP6ZyhIG46NfhdfgCpz6Sj7Q6Ki6GTLehHvqY6bRgpq0ErPHj4ZFHYFFQv9+T59x874UdVMU3cN91w/n3OWMxqa3zQy4FxIg+hg5R23ie18preWV7DQePnwMgyaxy19XDuHl8JhOGphJvUkiIax2gipb42touGiIOJ82r81xUv+N9U4dES+Q+rr2ZcRvNxLJhg7XVCsEZXta3DTdQVwO+jmIHelB0VCgCp2Jjy1PGvVu+3CDzYI09GMqchoZAUtSyZXDIY2UVhdhwcF4a/WezqeUAIyCohG9GhhEYncxOBtLAaVIoZaq/dWA1Vt7jS2zl2qivA4wyDoWFba+ORjaWcjdrEUiOMpB9+No8+urGK6gq/PnPoQQPsPfwae4v2s6R0y5+85VcFky9tFQ24YgRfQyt4NF0th06wfaqk2zdf5xPHCfQJeRmp7L8a3lMHZbO0LR+KEp76ekBRCv5C68DE97Yui0ibp2wdAynYpTDFYrwJ8OEozczbiMh0sSyZEnb1TRnL5+NalbxuDwIIfwTVneTz0zxJjwuD4qiIObNpeoNq58ky8qgvvIEb2xOI9Af1XC/VFfjl1gWFBiWeo3urTIp4Yg2iBEc4jNGe89mWOx1dYbP/gQWbudNahlCOXl4vMeXQCk+d17H3ykR56HfqCMo8R6W/VXlxhtMJI1UaWkyoaIy/koTZRsP8/GzG9AGmpFCMDq9lCPpKmpaM0LVuCLzDP2GJHHGfIo/1rXwx6Wh52hyawxIMvP3RVeTn5MecRyXEmJEH0MrjPqPdSF/P1Awkm9Mz8E6ILJl3JPwkViWy4ENB1nShlNmd0jErRKW4jv2k3clQ9VnSSdaEmlqaOqURd2RhV68tBiP1+WkuTWaGpqYvXw2ax9ai9Qk6xev9zfW7onks7MWG6+XWVGUQLbqa08d4Yg20F8kzKeq0TSjMXdwIbR/+RejVo1vgVBDDrUMZSo7sFJDPVnUMZSrZAkzRTFNOeM4Xj2IMpmHCRcev9Wv0EzH3y21fzOp9v0kTahBiTd8NzuBnZ9A+p2B7R55z/vLPRNC9k/jc9RzEOfRkIMT0BI1rhySSkZy65pMCXEq91xrIzMlQr2mSxAxoo+hFdIS42hsavH//dSmg7xXeYR/mprNvdcN71U/paPYQZbLwV36s6hoaKg8p9xNvdkWdZKVNayVXmfdRW3BX6fdS8ZCEajx7VvUwS6W8Ikl0ZIYunJxaf6y6r6Jx1HsQOoSqQdIvbslIax2wwpfMCugc/epS+K1Jm/npPBeqMb7wVm5jz9u/PRNFCBR0ZnGJwzmGMfUTHYrk1jbchPx0oWsVhkrd3HAb/FDP85xniQ6suRFnIeshVtQ+7s4uyubsxVWlKYkXnzJw+jxGk1uD+fcGk0u42fdZ8cpe2wr0q0hFIU535nG5NzBHNvq7NEKqJcKYkQfQyuU/+fNAJxqauG9T49w8PhZSqtOsmzdXkqrTvLEXVP9NWx6GrYCGx6lClU3mjILRTLzRgXT0ujdE76CVcGt9Noj+85a5N6YYgj5RjpGJBeLb2IJdksJIfxkLhTB8BuHU7C0wH/McFLvSgkF33h8+xQXW3G7A9a4lMbvp0Wql9/Dm2Abk4EvcFxcDFNcJczQi9kkCigRdqQUjKWSQRwHYLDpOLddVUz8JhcqOpqU6JgIrkdjkLyBSDJLIYy/4wefRu3vIuPECP7fHeNouM4X22ijl/DUbJzDLa3u0djrbVHdq8sNMaKPoU2kJsb5g1BSSoYvWcs7lUfYf/QsV3Szm31bwUSr3Yr5T99DPrQJqXkQ8WaGL70bOhlw7A3fu8+SDrHo27Go25KMWu1Wo1m39z2pGBUUEQapB5N8W6Tum6BKSuD5ZR1r6YMnHWFSKb3qAWAAwUQupcCaO4ATFcEkb5CyEHDTTbB0qXGe/rtKeESfhRk3bmnmh3kbeHKXnXT9NMLXlapF5/ktI5mJioIOCEy0ELpSMM6TnQ0vvWS84tO95+cbAWCLBT6sP0fxefinLwsWzY7u8+rMJH65I0b0MUQFIQRPFU7jR69UMPv3m5k6LJ1vX2tj9sSsTh+ro2Bi5qL5MGljl7OBeqvaZTDpRuOjtxXYUEwKmq6hmJSQcQSPUVEVRs0dRf/B/Vtp5X3n7UzSVaQ4gqPYgcfbR9ahZfHGplR0JMLf/VRBCMnu3cEBWBFi8S9YEPgoJjUUIxWjy5TETXpFMWqcnbzbbKhvKUiPhkThA30GD/JH/sSDKOg8wXdZwCucIQUPKr72fz/5SejHXFxslDv2vTZqr5niZ+HFbdX86JYxvbaivFwRI/oYIuLYGRdl1SeJMynEmxSyUvtx0/hM3vmX6yn6sIq1u+r57vM7sI+w8Ny90zvlt4/K4u5qNhBdqw7ZmWN36ngy7GfQcSYsv49XnzlBfNlH6G/s67RWPlxWuKaokYaiLUZD7xbdKMGrgCnehO3hebToRh/ZQwz3++Gl/38dRRhWPRgW/LRpsH17IFDb0BB08oIC9Dgz0mX0iH1fFtDi1qk7kUCGAA04qg+gjiE8zf3sZiIFfEAxN3CSgQh0rrYdwZyRxjfv7ceiRcYE5WvZeMhjBIn/9CcovEdjyT92kWRWWXrbhBjJdwExoo8hIgpXbOPT+tMhr908PpNffzWXH94yhgFJZn72ZiXbq05wptlDepI56mNfiPryvbFs76x23VHsQPNoIEHzhE5oJSXwjcWZuJozUOQoFlJEjrs2KjeTr9yDxWI089B1UBVJ44o1lLZUhUwqjXoKu5on0794EGXePrKG/DHgfzcU5ZL7//kcq/6R4l8h3Huv0SAkUlJXCXaWyA1cI4rZKG/gI64G4NVN6aSJLKyyhkyOcTer2IqdbeJqPpJXAQoCHRWNCY61DKupY8iUfEqfzGL94vWhjbh1Kw89BEeSazhy2sXz917FdVe0Lp8RQ8eIEX0MEXH1iAF+ok/tF8ep8y28U3mEbz71MS2azt7DZxjYP56NP7yB5ITO1f7oTYu7txDsblJMCnn35EV0swQj0ZLoD9yGJ275rHFdCiQKDmFjuPkwtgJbqwnF93eDZTRvl2WyYoVhxatqoD1gi0eySU5nJk1kcgyEcexXmU+1HEZcmQCToFazoqowf66hb9/+iUSXCkKR5ExI4cXrj7BudRPD81JpaBjgT6AKLy2wdCls9tj5QNoJLmmgo1JBLlalBnTIUWoZHv8qQx5ewF9/W0+85yznScQouFyD7oHSv5QiFIGUkuYEE1Uj00jN3MtZ9znQVYoqDnKlLZ1rR1l6/HP9oiBG9DFExE9vm8C91w3nhY+r+fsnTv/ru2pPoSqCkYOS+OHYwZT/7qMukfWlFigLcTdpGqVPlFKxqqJdaWVTQ5NBYGGJW+H15YcptczOb2DmvUaUMTxpav3i9ThcWazSr8YjpN+9EqxQkVKwl3F8xhi+rL7B3feb2Hp4JM7XbEgp0HSjNkxOToC0DR+/8FrsgjyLkx2Li6hvnszT78wFIYkzG6WJfXjySXjooUBlSp8XJVgpo40cTd0IC1fnuRiVdsz//bhxfhJ/f6yKt193ByZAL073j2PzrcM5Yu1vHLSlhRT1AEKBQf2T+M1X8mIum25AyItZR7QNTJs2TW7fvv1iDyMGL1wejfc/PcpL251s/OwYJkXwh2tHUPaV1T1ePqCn0VO14f0a+maP34AVqmDmz2e22RAlUtAZCFG/iDmzkW+tQ/doKCYV8vPQt+8AXSJUwfBZwzm04RCbtGt5n5nesgEGF8bFeVv+hTTMkJhU2LTZIMWOKmQGV/30FG+m6Mefs0JfGKSlN86VkGCUR3jwQYPkfa/75I+hPCL84wtulN5gGc03FmfidklUofMf174PW0uQmmTzXBv7Jw1kZv15vvn1SSSmjjZKGczQKLhOjZF8FBBClEopp0V6L2qLXgihAtuBWinlPCHEAODvgA1wAF+VUp6MsJ8DOIMRn/G0NZAY+i7iTSpzJmUxZ1IWh081M+/xLTxdUkVeD0gYe7JJR6Rj91QtG5+7qaKogvIV5eia3mF8IZKLKlhWCRqD63ZQ12IwtdaisXfbOUahEqcYYx6/YDzVm6sZ7qrCpGvoisAUF2gAsmsXfO97wWQv0KVBsEuWtO4VuyxMihmeUVyleND10KxYX2ni1atDm4iEWvOhROzb50+PnWHc28ZnsJkWXPogdKmAqlI24CYOj5nOoL2bSD1hNJe/95u5XHfzFQBccw3EnA49g87cxUeAT4EU79//DmyQUv5CCPHv3r//rY19Z0opj3d9mDH0FQxOTWBydirO+tPdDqiGE/GU5YWUN1i71ec1WFpYuboypKRA+GTU2UnG527KLcyNer9wF1V4IDp5SGg+wlmSeE4p5P4bHRQuNY7vK3twq+UEnzZktiLqSZOMRhrBjbctlsD7bVV43LXLIO+8PEhLg4ICK9/7k5lND0ncLYZfRvGKqcxmQ165ebORTaso8IMfGNmxvmqXQhi/Bxv3Z+rO+Ce2YRxCYYYh6JQKa9YIIJV4q51x08sBF3v7qVzX4ScRQ2cRFdELIbKBW4H/AX7gffkOoMD7+yqgmLaJPobLCOmJZiol3Q6oBvu9PS6NXz3oYJO0+mvKWxr2dTrzs2hWEZpLM6SFPqNUad2ntjvWfnfiC+FWPsD+tfvRWjQ0qbJLyeVYvJVZS61Y7a3PNz/CMe12ePXVgP9c04w2fsE69OBG3s3NcN99gQYc73grfcbHwx/+kMmcW+H11wM++PvuC9R9nzQpNL1h/vzQFUNRETzzjOHeMZvhnntV9uxS8TR7sMoaFlJEhcilVJ8CCNTk82R+9RPOavH82+yxfH16Tpfua2cgpeSsy8OJc27SEs2k9rv0Gol0FtFa9MuBR4Fg8yNTSlkPIKWsF0JktLGvBN4RQkjgCSnlk5E2EkIsAhYB5OT0/ocdQ9cxfkgKq3fUcNaWGnXD7kgItm6lonJAs6Hp4HZJ/vxgJdfJzZ0iYf/EofsilEQsKRCy7QWqXBmM4CqdjmIHcx6fQ1NDE2ctNhIjrGiibUre0GAQua8mja9/a0FBaCNvKSN3WXK5wt1AAX+8b1UQPo7wdAe73ZgU1hQ1YsPB1EkWpnpdXmXPlGH11FAlhvutflPaOYRJZ6oli+/eMKJLvnhNlzQ2uTnZ5KbhrJsT59w0nHNz0vvzRIR/bs24GaMz+/POv9wQ9XkUwSUZL+iQ6IUQ84CjUspSIURBF85xrZSyzjsRvCuE2Cul3BS+kXcCeBKMYGwXzhPDBcLozP4AVDWcY1xWSgdbt43wSoq/XGxFdYNJ0RmmHYqqlkzwisI/cfgsegXUeLUVycOF0fK3h7ZWFHPCtutMU/LguvyKYlj4UhqvzQk/cBsIDewCSOp21LPy307zwO+uwKMpmONFu+PIxknKqiKOuTWKVhnXlluYS9kzZSBhmDyEiRl4kLhr0sg4b+EjDpL/cydjByczdnAKYwcnMzKjP80tWghBN5xzc+KsmxNNgdcam9zobTBGcoKJAUlmBiSZGZKWwMShKQxIiqe5RePZDx0cP9tx+6m9h0+zcouDNeW1pPSLY/LQVCZnpzE5O5VJ2akM7N9GvZ0+hGgs+muB24UQc4EEIEUI8TxwRAiR5bXms4CjkXaWUtZ5fx4VQrwKTAdaEX0MlwZcHo3vPlcKwKiM/t0+XrBbYoPXLTDOcpw9i+vR3G3XkmmLKKMtUXCxtfzRrig603LQV5f/scdCm4a4XFBeHrrtII6QRT0ObJwRaYAxOYQreFQ0Bm1bxwvbbLgZjUTgdkmKi0XEZilWe+Sm7qeqT6G3GFa0FcOF48CGTTr4RuYVHJ41iV21p9h7+DQvbXfS5G4146AIw204IMlMepKZKzL6MyDJjCUp8JolKd54rb+Z9EQzZlPrjO1PHCf47nOlJCeY+P3X8yLeS02XbPj0CCu3Oig52EBCnMIdeUPw6JJdNad4/7Oj/lXJlJw0/vDP+WSn934Z766iQ6KXUi4BlgB4LfofSim/JYT4FbAQ+IX352vh+wohkgBFSnnG+/vNwM96bPQxXHBsd5zknFvjO9ePYFRG9wqbhSPgBshk6qT2SbgtouyM//xiavmjXVFE25Q8GG+91fq1fkHd+QZxhEU87S8DPfhHhZxJs1JdbawCfGWLZ410MOzA+1hlDYB3e0O+WVBgdBOLtqn7e//+Xsh4jELJNajxKlfMvJkvXRX4HHRd4jzZxMFj5zj7eQPnKo4w4bphjL/ehhpFs5uSEni1yIkNB9cXhiadDbthGIve28vJphYykuN5pbSGvfVnGBCU2V11oolXy2pwnjjPkNQE/n3OWL5+pZW0xMA2Z10e9tSeYkd1I3/euJ/b/7iVP/5zPteM6puZu93RLv0CeEkIcS9QDXwFQAgxBHhaSjkXyARe9fq0TMCLUsr13RtyDBcTW/Yb4qnrRw/q1fN0RMIX2vXSXRlouH872hVFtE3JfSgubu1+iYszgrMPP2y0BByv7CNO10BKFEUjN83BjCVGJcxVqwKTyg9+ZGL7g3XoniArXNiYfWs82QwDIlvv4U3dAZxbnSFjGjhuIMNuGBaSXRx8j4fZrSj7TlD0tX+guTUOmFXSNhRSg9Vf3bKwEOyE3tiSErirwMnX3UXsQ+PgShX7I1dR8jtDr6/Gq/z3SwvYn6Cyu+4Unxw6wWvldSFjEwKuHDaAJXPGcfP4zIh1nPrHm7hqhIWrRliYPXEwi4q2861nPubf54zl/hldizX0JjpF9FLKYgx1DVLKBmBWhG3qgLne3w8C3e9oHEOfwf0zRvC3bdX89t192EdYom4n2BvIXWh8tToqRdBddFeP35afPZoVRbSBWB8KCgz1jMtlENZtt8Gjj4YqZvIsNnYsDp0knSVOPMUOnn04kX3lTUxdYGPOIisZzGXtQ2vRNZ0cUcswpY6mNyRFb7dtvUPotW1etjmk/o5QBbc/c3srqWv4PQ5RZTV7eOexCr631qijD1D5TAnv8yUUjxtpNqNsfJ/iYjtDWxyoGP0MNLfGh7/+0B+g11waKbuO8UiQiODEOTdnmz1IJFLCwOR4+sdHT43DByax5sFr+dErFfzv2r3UnjzPf90xMer9LwRi2QgxdAoDkswsmTuOR1/ZyTuVR5g9cfAFH0M4KXSm4mNX0F2FTmf87MHoTCDWh/ZWAAHXmJWJk0It7vDOWTs2q0ycVMjURVP9Ov5T1afY8dQOg3jPe3j5qy9zw09u6HBlYivwtnZ0aQhVYP8XO45iB4B/+4qiCn/WsebWqCiqAIyxSE2ChJo3yumnXYebNAC+0vICAhcCie5y0Vi0hoJCO8/E2dDcKhINVRHI4Gboqmi1+ju38whVEcbfmUk2Kd7En74xhe//rZxXSmv46W0TLqoRFI4Y0cfQadyRN4TH1n/GD1+uYO2ueqbkpJGfk864rJSIwa+zLg9VDedINJvIGZDo97O29yC1996FlkZ2100U7Gc3mUKbbLeHrk4Q0VR4Dre4I3XOqiiq8BNubmEuR3eF6i3O1Jzhze+8ybwn5rUrs/W5qTYVOdh9eCAvL/+YoVp1SFmI8hXlgdISivBnHxsvYLwndYZziHLyyMZJPC40DELXUXFgw26H54qt/PWxQsx1DvILUnA8/qa/GfrcP87tcCVhtVtZ96STXz3o4IBu4+fx1la1/iNNah99JDi1z8I5dx3Ok00MsyTRVxAj+hg6jXiTyt8WXcUf39/Px4dO8HqF4eNMiFPIt6aTEKdwoqnF0Dafc3O62ePfNznexPWjB5HsSeEvv0jhXFU6cT+PC7FWO7JkL7R/vrsKHZ+VXVQETz8Nf/kLPPUU/PnPsGhRYLuSEmObw4dh8GCjw1JnA7FdQaTOWYpJoeyZMr9SpuzpMoTqta7DULm6kqmLpoa8Fk6INVi5f5UVV7NsVZYZCCH1wfmDqS+tN86lgGJS/K4XOyWY8DCSA9SRTRELseGgSh3OTYXz/Od/+m0rbrcV8y54cfngNpPvIhkNANseKmKGR+MaVJ5zFVJcbCUbZ6uCcz5lVw1WZs0CLMlkfBPWFJ/mkQUxoo/hEseojGSWfz0fgPpT5ymrbuSjgw2UVTdyxiVJTzRjsySSnmhmUHI8wwcmcc7l4RPHCbbub6C2sZ60OyDFZeLE2jyKizNDsjjbs2R7SxrZ3iqiuwodu90gcV8CkqYZyUm+7NWSEuO87iBZt9lslBjwtdPzNeXuanmI9uCLd2TlZ9HU0MSp6lOUPlHqf1/36Ea1qggYv2B8yN/BVnKtmkPat+dzmrSQsswNDGCYqCPRkkjGpIyQiXvKvVNYv2t9CKHWl9VT9nQZmRxjHmtR4hSEolLbkkOdOizEUg///nzakMmSJZk4S5xsXrY5pPzzqepTKKqCTqB2kaPYAZrh45dojFQcFBSEBp49Lg9vPWDIm9R4ldMLH8TtTqOf5QwAW3ae5ZEFPfsZdQcxoo+h28hK7UfWpH7MndRxW8GvTDMexvc2tbDgvlMk2fdiub2U+CvygCFAdJLCnpZGRusP72xwtD3oemASKy42FDHBaGkJ1ILvrK8+WkSKd/hIsHxlOZoriN29LWRVs8rQq4biafYw5d4prax5HyFWa0NZpX0T/QkVk6IxWBznsJrJMKWWPH03UpesX7w+pGm6j4R9cQHf328+8GaI1Z9/b36bNYcifX/Cr9NX/llzawhFkDU1iyn3TvEfxxSv4nFpqKrKj/5ouISchCbk+Zowai4NGw7M5jw8x40Ewqsm9i1NfYzoY7gouPH6ONavGsg7G6/iQ9Mn/LG0jFFXwO25QzotKewJROMP70pwNHhiKCw06sD4CD14EisoMGSQwRZ9XJzxemd89Z2ZiJwlToqXFkcs/Ga1W1m4cSHrF6+nbluQ/FDAqDmjuPbRa9vtlauaVRzNNjSpIqVCi6YxTq3kgfvd2KjiwFMyJPPZ1zTdh+CJ3FniDPHhB09IkcYQ6fuzeVmoi6ZydWWgObsmqfukjsNlhwGYumgqs5fPpnJ1JeMXjGfqIqt/TIUbCileWszBdw+GlKueV2ghrxDe35jC800mGtQTwND2P4ALiBjRx3DRYAQN42hyT+fulZ/wg7+XM9ySxKTs1O60jO0SollFdJZwfT55TQvUZv/jHw2yHzIkIHuEgFUf7KP3FRKD1mOLROidmYiCC8ChE7Hwm9VuZfby2Tx7/bOG6wZAh89e+4wDbx9oU2bqI8QBRQ1sfkrSommo6NjkIa7PMRnukVXRx1gcxY4Qaz7vnrwOV3Ph35/wuI6v/LO/v4AEvUXnxe9tYeW6wbSs3cVQrZrqzdXsPWBiU3kKcxYkMn+RlYKlBca+YQFeK2C3K3zyp/7UnGxqd3wXGjGij+GiI9Fs4qnCaXzp18X8fsPnPL3wwrQsCCfLjlYR0Wap+gi3uTlQstftNkoTvP228fuuXQbRB6OtyS18bBCZ0DszEQUXgPMVfhu/YHwr2aPVbmXun7xaeo/uJ8WO1E5Wu5UH7Fay8o/w5wcrGaYfwhZfj63gplalKsLPGY5wku6KnLatuM6OZ3ZQt73OWNEgeFmbj3NNFirf4m5WYXXV8tRjjWxiEivf0XiJI8xf1H6MqC82c4oRfQx9Aqn94rh1chZFJVV85S8f8s/Tc5g7KYuEOLVXzteW9dveKiJal5KPcMOf97q6zsslgycjnxunujrycSJNRL6VBYSuEBItiQghkIqRLTp+wXi/zzo8KcynpW+v6UpbssP5izKZOsmNo9jkJ3kIkHo0iWg9FXwPdwf5rldRFc6QyN/0f8JJDiDQEFQwmaHUc5DhSBQ8SNatbmL+osgxIt892H36NLflDunSGHsLMaKPoc9gyZxxZKf346/bnPzgpQr+641KZk8YzNzJWVwz0kJchFT0rqI3Neo+wvXVfwcjW7WgAMrKAhUlg1cEHblifI09PB5Di69657/g40Sy/IOVPCtXwsaNRnXJ9YvXI3WJoip+mWB7uQntNV3pKHPY93u45d5WPkSkSaOng+/B5waImzYZ5zaD5H0YMn0o1iuuZcQLhwDJYYYwZ0Gi/5rDG7gXzSriPBLt+/lUHmrVbO+iIkb0MfQZ9DOrLLp+JPddN4KPDjbw0nYnb+6s4+/bnaQlxnFFRn/SE82MGZzMlbYB2LtB/l0pFtYRgsnaR7gWS0AeuXixMbEoitF/tS3f+vLlxj7btgXcP8G15KF1o28fgieiZctClTy+Ce06gur2C/xa8GhyEyIRbkcJbG1NBJHO2ZPtH9tDK599QQbqNqPIG0Ccyds0ZfGHzBQaN4jNjP7hbcxflMu6J51se6gINA01TiHvnjzAcGedzDQmAtcZV4+PuTuIEX0MfQ6KIrhm1ECuGTWQ5haNTfuO8U7lEWpONnHw+Dk27D2Kpu9nYH8zX56SzVenWbtUMnnhQuNnsEujLXSkZolE1hDQyS9bZrznqwzZ0BDYN3h14XIZDbh9DUR8MJmMCcLXuSmaMYcrecxmyLM4OVXWWjveHfdIR5NEe5VG2+up25tZz+HndhQ7uEcpolyfBAjmTDmGqUwzMoal0cxcL9/JmicH84cHHczwGDp7j0uj9IlS1DgVoQjSG84DYB8+oMfH3B3EiD6GPo2EOJWbJwzm5gmBmjrnXB5KDhgW/4oth3hy00FumZDJf90+kcGpCf7t2iLncFIuLGx/DMEt+uLjI6tZwsn6oYcMovb5/9tbQYQ3DNG01k248/Ph3nsDuvpo3Uw+JQ/A7flOdiw2rGXFpDDl/ikhBeG66h5pa5II7t/b1kQQfs6Q2EEvZz2Hn9sWvwmrq8ZY6WyH8nIVxaSgSx2pSw6+d5Ct71dyQBvJNd7SCyBQpY7m0RBCcMSaDMC0Qcm9Nu6uIEb0MVxySIo3ceP4TG4cn8mxMy5e/LiaPxfv56bffsDj38inYExGu1LDcFJeutT415al/uCDgYxWlyvgzw+eSPIsTq4XDg4oNuoUq5+sfe6SJUvaLzYW7OpZvDjg31cU4+f27YZSx3cd0erlg105wVpyHZ3UnNR2ib0zmvxw0oyUoBTcCCbSsX0B0uDYwYXqFxCikX/voKHC8WgMmWYEVX3KnBx5kGZMgOQQw0nmDJkcMyYnCbuuyqLfuRbSKo9HbvB7kRAj+hguaQxKjueRG69gfv4QHnh+B/et2s5vvppLZfHQNoOt4cHSd9+FzZvbttSDrWtVDahZfBOJTXWyUBRxg+bhBqGQ8rW5/Mc/pray3tsL5Aa/5ysnbLHA6tXw3nuBpt4+67wrmbKdqRHUleSwYIS7a5oamvyFz9o6dkjPXwF7y5p4Z1mEiaYb6ckdlbkI1sijQ90ndSgmBdWkonk07HoJf+YBf9OW5yikRlgZc9sY3v38GIdzkrEX1zDmd1Mjnf6iIUb0MVwWGGZJ4m/fuZr7Vm1n8d/LuXtiC2azLaKrxG43fOgPPGD8LWWopR6M4PruVuHk+7c6yMbG88VW/0QyVHegS6/JL3XOvrSW1X/MoLzB2m6NmrYkicGkn8ERNm4YiI6ClIKVK43Xo1EMdbXZCXRdleRDpEnFN55geahv8rLbQ/cRJpWfrrDh0MImmm7MQJF2zSb0M/DdI39GsDeRauz8sbib3Ax5dzMm6UHxJhTYhIPDJiue65PZOLY/OULhv3994VYi0SJG9DFcNkhJiKPo29N56MUyVu7aw9z/PU5C40BmT09j6pUpQECh45M5BsNiaX1Mn1vlr485GfBGEWde1yh6W2XK8kLMZoPsa1UbQlOQ3uxNXdM5vrqYvAUFLFhsjchJ0ahLnCVO9iwuIle/he1MAYTfhdSRYqg7zU6g66qk4MkreFLxVXf0SUV9sQgpYcUKX3A5MBFtqrbheMraeqLpxgwUvuuaokZSVrX+DKx2K1lTskJKPyQNTuKawmvYULwFzf0BEg1hisMiTzJB7OD/HZhMzoD+vPb/CkhJiIvuZl1AxIg+hssKCXEqf/nWFH6/4XP+us3J8eYjbNkEP/tQYcKQFHKz00hqTqPoH2lImYhPNy2l0WqvrMwIfJaVhZYL3v2mgxmahvAqLfo3ONiwweq1mK2YdwU6MaHDwfcOcnBjNYO0Qqp1aytOiqamvm+bXFlBObnoAsxmhcJCgxjb81501yLvSr2hSJOXz13z/LLAeACmToVPPoGB8gjjWvbxfpENuz3Q89dUAv+zKsJE0w1dbPiuNhwca+MzyC3MpXxFOVqLhhpnZOPWYGXH3B9z5rM8bhi0B73ZTf0ntdw3fRn9+x+idFUee+bEXdDSHdEiRvQxXHYwqQr/evMYfnDTaGobz1PhPEVFTSPl1Y38/RMn51scDLoHtHNmzh8aRLNjEOcPDcTdFM8TT7S29AH6k0cOB8ihChVBoiWRqcE+d7uRPRoczMNb4rZWWP3Sxs3LDAs3Gn+5b5scdy3fVl8g7dvzmV+YFlIfpy0UFBixg6G6g1rVRkFB510Jna031N7kFU6y994LDeVGb1dVaugrVZyFgVVNmxNNNyrehe+ajYUib82dWjWHN6snYSoJrHwWFi/0r0jW7bLyve+BplnJZh5HPj1BnPDgUYaSdHUVTfsyaaoZ2OkJ9UJB9MW6DNOmTZPbt2+/2MOI4TKER9N55d2zfO8njZiGNJBgO4bSz8gqctWl0rhlNM2HMoL20PFZ/QKdu1nFMFGDKaFtd0uwVTtleSHlDVbyLAFpY3BnpY785V1tSu4scfLszCJ0t4ZiVpn2B2McPVkNNDwG0JE7Knz7Fx7YzP4nNoKUCFUw8+cz2+1U1ZPwjWWc5Qj1ZfX8YOVkWjxKRLd/SQnccIORfJaNkwKKGcFBFGDrzcPYlz+I469ORXcO7tES0p2FEKJUShmxUFTMoo/hCwWTqvD12SkMS02huDiH62+QHGo8xap3jlGZUEvmVz/h9LbhnPxgLOgCg+S97h0UdjGJYdLZbsp+eMBzDq3L5EYqzRsJXdW2O4odSI/hBNdbNH71oINN0tpj9ewjxwDaD/aGrxDG5idyQAGpg6Iqvd4pLPLYM1m4MJMWT9turuJi471snNzNKhRvB5bNc4axf/Ig9G0DGZ+Wye+f7ZvWPMSIPoYvKAKkI7iWNL41N41FD4zgldJPSZl+CLW/i+Nv5GIEcL0dN3wQdJiyH05yF7r9YfD5pKJyQLOh6V3z10dCWzGAjiam4CSqdQ+v89eauZCehfCxQ6hbaZzlCJuXBVoPjrMcIU4dSK5egYqGAI4MTWL/5EHIU3E4i6dz63dEnyV5iBF9DDH4cU+hyqqCiWhnEkgv+Iy4jNO4qgfgPpLKeYcFTseTH7ebqfdO9WeURpuy31vtD9tC8PnOWmz8crEVNdr4ZZiPJZL7KNjnfp1awjeqi6GkoN0ZJHhSFEIEasxjtCrs7SbvPoTHC4KD2+MsR9iz+OmQRK89i9dzl5ZFE4Gs62NZRsmNhs3jiDOJDrOrLzZiRB9DDF74SgasKhpJbVM8p7NrqbPU0iKqEVIw1Z3II/MLmHDDcP8+nbHUe7oCY0cIPt+GSVHGL8N8MkeWv0jR4j2tViy+wObnRSV8c+Us1KfcsCqoIluEE/mCtUO1amzCQZUYjlNmA62bnkRCV+MV4WgdlDWO+y1vzZtInaiydSdHRQaaVGkYksCO64eSeCqeb84Yyt1P9V2XjQ8xoo8hhiAYLh0BWAErui451HCOpzYd5KXtTr763j4mvJyMRRvYSvt9ISz1rsJuBzteS52Cjovpe/0aTavXobmHRlyx2O1gLy4GT1hFNl8d5uBgQEkJk6rf5LT4jJt5C1VqYDLz4e3/TePgsf5mIsHNu4PR01Utfa67SKUaInWi0twaWWoD9fpA3p83hPhzOn/7+kgmF/Rc6ezeRIzoY4ihHSiKYOSg/vx8/kS0M/14ee8+3vv0KCffH8iKFT7ftGE5lz5ZSvHSYm+f0YuTAt+m1RttRmmYXyNxwRzUzXvaXrG0VZEt2HHvPXea281cBEYvJwm6h+umt8CSeR0SeTR5B11BpFINwRN36S4z+yZ9mUlDGhg7uBHHhgM0p6vMfP0Ap4YNgILhHZ+kDyBG9DHE4IWzxMlnGw/hmpxB9VkX9XuPkz5qAKdTzbxRUcfxs248Z+M5v9+QX7a0BLis9MlS3vzOmwAcfOcgwAUn+3bJMtoMqjC/RqbdTuGkqW2vWHzb+5rdrl1rnCM4GBB0bqEoYFJbdV/piMh7OpgdTWXNTWtOcPdj49EYhIrGs49W8um0MySecTOi+swFUwn1BGJEH8NlDSkla8preWtnPWeaPSQnxJGVmkBWWoLxM7Uf/eNNbCg+yMtv7qVuaH/0LWcCB9hdR5wimDk2g9xkK//6jUG4mo3lelxcgMsqV1eGnLdydeUFJ/p2ybIzGaVhOsioYgurvGmsJpPRFSW4YH74uSP48Tsi8p4MZrdXWRMC7Q01qZBLC/04TxU5vLoNaq9K4Z/i47n73d5piNJbiBF9DJctdF3yH2t28ddtToZZEslMSaDmZBOfOE5w6nxLq+1Tks2M23GUwdVnSD9+HlOLjiJg1v+bwaxCIw9lyvuRe7COXzDeb8n7/r7QaJcsu5FR2iGCVwtgtL5qqw5zG+eOhsijlW62ORF41UQN1Wn+CdHT7KG+rJ55/zcPIERFpeDhVtYC0BwXx+ppkwH47oNXYc3oW/XmO0KM6GO47KDrkgPHzvLXbU7+us3JAwUjefSWMQgR0MJ/ttnB0197hdMJKi3JZr68aBoVf3jPX57WB9WsMnpmwA/bVlkAn/VeubryovnoOyTLztY0iBbRrBaiOHeHK4d2agxHdFtRE9pA1xujmGyKo0y5i2ptKNnSSeJTWziS7yFz0XxjsjQJNM3jT5MDBbPeQhqS4wjmPb6Fr02zct+MEVgHJEZ3jy4yYkQfw2WDvYdP8+eNB/hg3zG/xT4iLosZKaEkD3B0i5Pko03014z0+9TTbn/jiUPvHfLXRM/7dl7US/Spi6ZetCCsDxdawgn07mrBh/aCySUleJY+S5ZLx6lno7k1GorexLrqXwPbL1zoX3UowNVTXejbnBSyClXzwHffh3W3Y330UQrvUXA88QGJ8iy7mIQVJytnzOO4anyHmlt0VpVU8dL2Grb9xyyS+2C1ynDEiD6GSw5Nbg+O400cOn6OQ8fPcvD4OXYeOseBE6cQukqWNpjrhg6g6NcDqDmWxI2/aS0yieTmCG484XvdJ/vrs+hGE44eRW+tFsC4xqVLA51iIih6bC43d+kKzykLqTfbsOEIbSO2Y4dRHxnAbGbAvXcyYsefUT0eI/dZ6rBmDaxdi/Xxx7EmfAIuF/l6GbsHj2Lr9FHckW1m/o25nHN7aHJpJMar9I+/NCj00hhlDF8YuD06ew+fpsLZSEXNKc40t2A2qSgC6k81U93QxOHTzSH7DEhIoH5fEq5jOZz+eCSHzvTjY9XghPCmIm3VSw/um9rVoF93OLdLyUDdbQPVlxDp5pWUGAGRlSsNiZOvt2IERY/QNVQF0kekMeBH95E26SCsejwwOWzfbkTPvYHiTN85HngfqWuBAhctLVBWhvOW+3BsP46tdit/tH+VlOZz/PfpSpLH3nQh70qPIUb0MVw0SCmpPtFEubPR/29P3WncHsNJbkkyY+lvpkWTaLokMyWeK1Is7FuXhOt4Ei0nklDOJpF/s4myNaHH9sUFwXjOLZb266UHoyvuj+5wbkf9Vds6n2tpMTe4DJLrsSI2FwNhN6/k4RcNzi/7HXbP5kDdaEWBG28MbfDrjQ9Il5tm3cxvDn6ZHYsz2bAhE/uGDca2vl6MHk9IoDhz0Xzgz3jrDxvHM5lwPv02RZ5voJHOqYETeGd0Ho98/BLJ//vdC3pbehIxoo/hguOzw2d4vaKW1yvqcJ44D0BCnMKkoakUXj2MvJw08qxpDE3r18q3fuedcHJL4G8hoK6OVlCDLHpFMdR8vZV0A91r9BE8Lo/Lw9qH1iJ12WYGqI8Xp7gKeEc3009xIzrZhKNPIejmlTTnM+uxm3FjxszNfMx0JrHH2E5RYMGCiIqeD5YW8+P3Ctiq21F993+J3SD6zZsNy16I1m3EFi0ymvQ+9pjxRRoyBMdrJ9BQkSiUX20lXtO4e+miS3MS9eLSyN+N4ZKH80QTf9q4n9nLN3HL8k385YODDB/Yn/+eP5G3vn8d62ZP4PsnNRZakpk3eQjOvYn84heCkpLAMZ58El57LfS4itKa366/Hv78Z0hIMAg/Pt7biMPrlxeq6PEKkj7hiap2uvFRyLgURUFqMmQyCoePF7fqdm5WNvDBjT+/tN02QTevWMzEjRkNE27ieJPbDW2+b+b+/veNZr/BXwy7nfilSzhsyuZ6sRmb6gzcf1+DYN/+ixeH7uvD229DaSmsW4dNdaKicSbVxMFxA7hzdCbpN1zb67ehNxG1RS+EUIHtQK2Ucp4QYgDwd8AGOICvSilPRthvNvB7QAWellL+ogfGHUMfhc/Vet31OkNGN/H+p0d5a1c95c5GAKYOS+dnd0xg7qQsBvaPx1nipOLnmylfWY7u0f3NOsJ7rUKgjIoPQhiE3tBgEL6uG68lJBhGWmshSNv+9+7GNLsjPAmOCyRaElm/eH27GaDBasYdZjvxS+3Qlzi+szfTbufI8hdpWr2OKYNGY37BjRuJmRYK5qfB4PvgqaeMD9jlgieeMBK0gia3bJzcLZ9FkzqqVMjmbiipCXQj1/XWgVwfwvIArPffQiH9+M+EcZhMCj/4Wh8PyEeBzrhuHgE+BVK8f/87sEFK+QshxL97//634B28k8OfgJuAGuATIcTrUsrQNMIYLmkcOn6O4s+OsmXXWdZtOYuS0sRfXm/G53WZODSFR2eP4bbJQ0J0xz7ftKfZY8iVAc95Dxt/X4HbHdoYGozn1AdFgf/7P2Pl/eSTxt9SGv/ee89YrW/YAEuWhI7V538vKTH6mIZJrLsV04wkPHGWOHmzqAEHtpA2gOEIjgtkTMpoNzAbaVLpK+KbrgQrnCVOb4XMoahmJy8/+gk7yyUFCyzYFz1qHHPVKmhuDnzIYYTtKNqE3uIBFPSWFhyPvYz17R8HsnWDFDetllsR6hbHjc9n62Mb+eq0bDJSErjUERXRCyGygVuB/wF+4H35DqDA+/sqoJgwogemA/ullAe9x/mbd78Y0V8G2PL5cf5n7ad8Wn8agHgRhxT9aa6yoJ1O5I4b+/GzhyzkWCInlWwqcuBp1vwk7zPW4yvLGRaXSxXWkOcyPt541oWAH/7QIPmSEmM17oulCSS6LnC7JMXFkZtBhHPRLbcEOKQnY5rOEif/XbCBFe5voqHyh5U6729UOjx2pGBwOJEHTyp9SnwTZbAiWGUUHjtJSVNY8nZQkDy4ns6KFa1r6WA0+lbR0JCo6NjqtoZm695/vxGIjTQTRpg5n36zEk1KvnvDyJ69PxcJ0Vr0y4FHgeC830wpZT2AlLJeCJERYb+hgDPo7xrgqkgnEEIsAhYB5OTkRDmsGC4WXq+o44cvVZCd3o+fzBvPIFcmK/7Yj9fWBIKn19wFOZbI+5eUwONPJzLb64vxkbyC0Zv1znwHlvmhPU6XLzfcN7oOjz8O8+cbz6bL5XPpSBRvWqui64yznAAyW527qChA7M3N8MYbAZeQqrY2+Hwka7G0WWo9IhzFDg60WP2BPbdbjyjz7Cgg3BGRdycQ3OOIIks2XGW04OEhzBBbOKQMMzTwkWInvpnN1yEk7EOwFn6JwhV34WgZii2uFuu9j8KudaHdRdq7KUEz54lzbl74uJo7codcMpmvHaFDohdCzAOOSilLhRAFnTy+iPBaxJ5hUsongSfBaA7eyfPE0IvwaDoN59wcOHaWyrrTrN1Vz47qRqYOS2fFwiuprIhj1m0GafrgU7r4EE5sxcUQrzV5E8zhJOkkcwaJho7hmw4qLUZJCaxe3drVWlAQiLMp6MxhLc0kMlypwtJwBeFEX1ICzzwTIHYpAy4hIeDb327dGHrWrIAcW1GMlUU0VrOtwMbIuA184DYsTbNZUFDQ+drqHRF5Z+qV9TraCVb4Jsy06ga/BZ/lcnDF737OGN3DDULh3C1fJZkajH4AtL+UCTuvtfg5rMHbToq220roaUrOHOJ8i8YDBZeHNQ/RWfTXArcLIeYCCUCKEOJ54IgQIstrzWcBRyPsG/SJAZANRBDDxdAX4PJolFc38sbWU3y07xQtiWc5p7loOOcKCYKOyujPT+aNp9A+jDhV8RNR8DY+7TpEJraCAivPxNnQ3CYkGgPjzzDikXmUFZ+mPsHGr39vxeMJFDtcvDiUbIMJ7dZb4fXXwaIfJ4+dKGjExanYClontxQXG3JqH4QwJgpf1dzwlnC+a/NNBm3F8yLBarfy4+JZ5BXtCfHRR2oU3h7Rd0TkF6ICQacQgYyDVyVxpsksFa8wTXxEmjiNonkQuoZAI/m1F+HtfwQi8OFLGWj7QsPPG2W2bkkJzJqp4XYL4vu3YHvEwewJg7ki89IqXNYeOiR6KeUSYAmA16L/oZTyW0KIXwELgV94f74WYfdPgCuEEMOBWuDrwDd6ZOQx9Bg+PHCclVsdbN1/nCa34dP0nElA+zyZqyalkmOOZ1ByAmpTIrfNSGHOzHhKSuDXjxnPW7g0GUIt+nCdePHSYgqWFvD447D9mVyyhsAtj+ZSg5X7Hw+4VcB4xlevDpBtcM4MBKxtKeEYmayikBHCwc33RHaJ+EjT5TL+josz3EBtuWSCt480yXQEq93KA91sFB4NkfdmBYLOoC2XVPCq5Gr5Mf8ifkscLqRiQigmcOutA60QupQpKgqUQ+7BYERxURVu11A0VFRbPedaPCy6YUS3j9uX0J2EqV8ALwkh7gWqga8ACCGGYMgo50opPUKIh4C3MeSVK6SUe7o76Bh6Dh8fbOCbT39MRnI8X54ylMoNg3h9ZTrauXgA3ng58PwJAc/Ewdy5sG6dYRmbTMZzGJyJ6nNv+Mhwa+MEnpNDGEsl0/QdHHrvEB+/38RAeRwhNerjVIoG51JD6MpACON5XrDAUNH4nm9fYuSyZQEC9qFOsdIQb+UXbTRrttvhb384wspnNJKHJPPgo8kdum59JNtZH31b6EqZhb5C5NA2mTtLnKwqWIXWoqHGqSwsXhixmfiXRDFxuhshdSOr9977jQNECLTqpjjQJZjijKSfXghGFBz+O2Yewo0kafRhBuka+da0bh+3L6FTRC+lLMZQ1yClbABmRdimDpgb9Pda8BZ1jqHPYf2ewySYVDb+sICKUhO/+L/W5QN88Blba9aEvh+ubQ/OUn/ySVjy2AAgnf2MQAAZ+lE+10dRwAcIJC0ujQ+ecPCx2epXwamq4S/3xdAiuVsLCgL6eR/GjDF88G09/84SJ3sWFzHVraHuUsl+tJBQ72Jr9AbJdqXMQk81x+4O2osvVBRVoHlXhJpbo6KoIqS/rG/CnGcpQCw2B6SPYHzQYYFWZ4mTDbIQKwdwypHMSbmCTJ+ONsplVYey05IS7G/9mA28yjvmWTxnu5I70s+3ysi+1BErgRADzR6N2pPnKSpKDiH5aGAyGUTr2y/Y4gbD7WJAAJIdYgpj2cshOZwZbPEHXw9KGx5P2yq4SGRrt8Of/mQkSvrI/tNPYdeutom5N8sg9CZ6ujl2V9Gd+xf4DO0wKUgu+dRTgQSooMQHR7GDam0oW+R0HC02zv/mVR6QmjG7L1/eYQJBVLJTb9DGzkfsysvCY7Jzm+dw129QH0WsBMIXHIuuH4ElKZ67ntzO9orWXZfagxCBWjJgWOF/+EOovjsxRJ0mOKJkkX+HlWPx2TynFLJZnckK9T5qhWHNFxYaz7rvGM4SJ5uXbcZZEqzSDRr/Ipg2LfS1wOTSGr1ZBqE3EYlgLwbau3+5hbmo8SoIUOM7KPFstxszus/vF+yXDzpXrZrDKgp5X87kB9qvKNGnG1+6hoYAk//kJ8bPsNIGkdRKrVBQAHFxnDfF8+SVX+a6qgpyb4yoAL+kEbPov+DISu3HHNtInttdyaD6LWQzipogV0aexcnQhgoAKsgNeU/K1m6esjLj92BrKti9IlEYMH007z8KxcVWLBYr3/8+4Ibw1XK0VmxBAWzbFvg7L6/t6+3J3qO+MV4Id0pPN8fuKtq7f1a7lYUbF0Z/PzqQE1ntVtK+PR/9CRNSClowUSy+hN1cYWzbge60rcNv+fw4//2WkbMZpyqYfvYa+080c0bE8b2bc/pOMKQHESP6LzA8ms6KV3bx9x1O+rk93FS7FYUPWUUhNVjJxsm8hlWoGGyeR4X/PR98LtaWFoP4V66E/HzDqg5WqphMoa5V3zJ+2TIjqCul8TP4WY3WTZCWFlhdCGH83R58x/BZxV0l6AvpTunpCaq7Y2nr/J2KPUQhJ5pfmMbjPqGNIijIPwf3Lg9s285E0dbht1edYO/hM+Rmp2LpH09LYhyjUzQykuO5et6U6MZ+iSFG9Jchzrs1tuw/TmnVSQ4caKDh8FnU9ARkYhznWzSaWzSaW3Qaz7o459EZcKKJgtcPEt+ioQMz2cBGZmHDgeIleQEoaNhwhBD9vfcaP594IhCsfeCBUKVOfLzhUo2kWGnPqIvWii0oMAqZRZsw1FMEfaH9/RelTWBvo4NIt5+si6ooWLEQe+kW2GU23mxoaPuL1c7h78wfyp83HmDkoP789mt5PXYpfRkxor9MUHOyiY17j/L+3qN8eKABl0fHJAT9GpsxuTTinKfInpTJ4IGJJJhV+sWpnNx1BPmPfeR83ggYKcsCGEEVw1jFWuagoyKQgERHxcEwQDKUGkabHNyeb8OJNSTTNFyF8/DDbT+L7Rl10VqxnUkYcpY4KV5a7G8C3h2C7ivulMsddkqwH1wKns2BCpa+UqZd0NMPsyRx34zh/Ln4AN8tGMnoyygxqi3EiP4SgqZLjp91cfhUM4dPN+M4fo79R89SUdPIviNnARhmSeQbV+Uwa2wmLS9XsvlX25HeBtgzf57FjLuv9B/vhe0VfPr5J36CD3aRm9D45vwmHCxk95rPacGEAxs15CDQGcs+Zni2sObhYWwb9c9AfMQxSwm//W3bz2R78jfjPSsFBVasHTzH0Ugg/Za8SwMdUOgWQUc1EfWZspKXKCLVoFBVwy/fmTTlMNyZP5Q/Fx9gV82pGNHH0HOobTzPpn3HqD15nqNnmjly2kVzi4aqCFRFoAiBSRHEqQouj0aTW+N8i8Y5l4fzbo2mFo0zzR40PbQM0IB4ExOHpfOVqVa+NC6DEQOT/Bpg58zhfGje7Lc4z1psLPOW5s3Gye9/46Geu8hlJ/mUoaL7yV41q3zlUYO8nnzSyne/622gjI6Cjg0HTrJZ5f4mnsq2v0ZCBEoOBPduhfblb71RkdHvatElQhEMv3E4BUsLuuUOaded0qfKSl6iCK5B4UuLXrDAqInRjeI+ew8blZQGJJl7dLh9FTGi7yVIKamsP827lUd4Z88RKr2lfFVFMLC/mYzkBPqZVdweHU1KdF3i0SUtmk68SaWfWWVAkpns9H4kmk0kmlVSEuI4vus8x9+vIXHnZySfbCZREuJjDleB+CzOsxabv5mHqsLsPJUzWiI1WKkhhwom88/jdzJl9Fn6D+5PbmGu/5iTJhnB1JYWYxrwTTUObGioBK8FhDD+BdeG8UHXDXVMSYnBdyGiCZfk2aUOjixI5NOGTKqrWwsqsulY4dKeCibc1dJdku8Qfaqs5CWK8CCOL0mjEwXLImFHtdEjacYVA3tytH0WMaLvQbRoOtsOneDdyiO8W3mE2sbzCAFTc9JZMmcss8ZlMnxgEqrStay7dU862fqjVxiKx+9q0VTh9zG3FWS02q0sW2Y8K1maE5vmoGzbMOq4AgUNHYWjcVaueSQeS8O+iHVKDKvcGLeOggMbw9UqzAp49ICE0vevLaxZY5RP2Lgx6Bl2SRTdQ/27u/nqO7PRFYkpToT0ihhnOdJhALWjIOsFV670qbKSlyjaCsB0M1259uR5hqQmYFK/GKlEMaLvJhrOuvjwQAMbPj3C+3uPcrrZQ7xJYcYVg3hk1hV8aVwGA/tH9l93FqWrjeYKCgGrOtjH7Ch24HFpoEs8rtAgY0EBDFOq+YZW5CV3lSJRSN6VJsZOScSe72LP4qcjkmR4ITBzHCy418K8wtF8G8Xfre2pp0IDsW3B5TKSIv/v/wzRxNO/OsnI/W8TL10Mph6nngNhWbLlRfVsbL4am3SQ466NGECNRgVzQZUrfa6s5CWKHq5BUVl3muLPjrFganaPHbOvI0b0XUD9qfMs/ls5+46c4WSTkU2anhjHzRMGc9P4TGZcMZBEc8/eWmeJk8GJp6hCQaIjURg2P49bHg24WM5abLToqkHkukp9YyJvPvAmAFn5WXxZK0ZF864GNPKVnfzb8nne0rmb2yRJu92wwP/6mBNznYNZ99qYsygvJM4IgcKCwdZ9sBsnHL7uUIOaz3IzBzHhoZDnWM8tpIjz3J5vY+IkeLOogcVPT8YtQUXnXvX5iAHUPqmC6UvVyL6A+OhgA5V1p8lKTSArrR/JCSa+/7cyUhPj+OHNoy/28C4YYkQfJTRdUn2iib31p/mPNbs5cc4NQLxJ4a+Lrmby0NReWwYGuyTi4hRE/hSm3ZvLnEWhlml5g5XnlEJydAfnRSKm36xHaEYkVKiC/mGB3GuvDaSFd0SS2TjJfNsYw45dKtC6gbev2xsYSVMNDdDYCI89Fno9ihKoX+V2Q46s8k9AJjRuFetQNEnp9xVKJRS32GmRYCj5BU1zF2C1J7fyx/elpKIYLj6aWzQeeL7Ub4z5oCqCZ++5EksPrbQvBcSIvh2cdXlYu6uef+yooay6EZfH26bO62KPUwXP3XsVU3LSe3UcwS4JgU7B/FRmLGpNYgUF8PN4K7VuK9eLzX6SBwyliSqQmkH2QlV44qNcHFt9RG2QZEVRRYdj8DR72P5M6wbeBQWty4UXFweyVn3w1cbxuYSqXTY0XUWiAQIhjfvsq4Rok4bLyrgahf6Dk9uNR/QqwX/B5JIlJYHJu6NufD160h64x5v2HeNkUwu52anMGpfJyEH9OdHkZnxWClOH9e4z29cQI/oIOOvysHX/cb7zXCkAIwYmcdfVwxg9OJmxg5MZnZlMQpx6wcbTnrUd/kz4yDXPYqP0+6qhGQfUOJU5j8+hvqweMOrWOJ4KJepvFUDFKqPUbPmKcvK+nedX39gKbCgmBU3TQIIsK8em5uIIauAdSWTiy1o9fz5wPVIa7y1ZYvjoV6+2cqi5EOcmB+dIZA7rkWgoiopJlVg9tXxTf4E1Yj7n49KYmVLKq4Vb8TR7QHYv6alTuEByyb5QjhiMyy0oMC4XjPIWGzf20CW3ReZdvMeRDpdnTWN+3hA27D1Kxbv7SDSr3DB6EMnxJkZl9Ce1X1wPXMilgRjRh2G74wT3FW2n0bvc+8FNo3n4S6O6XJ+6o4e2o/d9SUMTlt/XShHT1jNhfNGtTJy00G+hB8slASwl8D+rQgUhIcFMTaP0iVIqVlX4reW8e/IofaLUiATrOv9+WwWHmxxMXWDD7j22r2yxyRTw3S9cCIcPw9q1oX0lfD56oyy5Fc1ktA88SgajVAeP/tnw0fvkoWMa0hjZWMqnj70ZuEGie0lPncIFkEv2lXLEYFxeS5DXo8cuuT0y78I9butwGSkJLP96Pm6PzkcHG3in8jDryupYt9soQ3zr5Cz+8PX8LqvgLiXEiD4ILo/Go6/spLGphaJvT2f68AHdstw7emg7ej/0C5zJhg2ZIRmiHT0T7bkxIglCnNhQVK/VDq2s5dzCXL/Fr6gKh9eVo3t0dmxWmTjJaOARXAph167QvJbgln1gSKKDO0Tdf79/5BQWWv3X4ruGOcBzt1SGXEf6yHTuLLrzwpDhBZBL9qV6+d4Kvn6Lvscuub0vrsVi+Po60bOxo+fAbFK4fvQghjc0k/Cr19l+ZQbl9iHs2N/AF4DjgRjR+3H0dDPT/3cDAMu/lsf1owcB3XMXdvTQdvR+R1/gtninvVWC771ESyKehia+5bWE33yggrOHz6Lr0i/dlAhMpoC1HBzsPFV9ih1P7QgZ+xYMV5CvEuWvfhUgcrfbIPklSyJntfsac3d0j8cvGM/Bdw7iJBsHNr755aGXlVyyLymHfEltPeqjLykxtLi+sqfBX1zfEk/Xjaw+X3ORDhDt/PvJ+wd548sjOZKdTHKji4f7J112naTaQozovbj/6Z0A2FJTuCNvCNB9l2xHD21H7wd/geNMOmnVO3GWWCK2Z/Nb5e2sEnzv+Qp6CUWgxClIXaK3BDSQAtAAB8MZc09o9qhvleAscfqte9/YTYQ20j5wINCYJPgBDM5qF8JoHBLNM11SAsUNU3F/08Jzf83GI1U+fFwwdv4FjIv2slyyrymHevRygx8oVTWWcMGzR/gXw9ddPooxdjT/flp/mv9xN3NmYD+uebeKcZ+dZMa7bTQWvgzxhSZ6TZc8vfkgGytOUXH0GK7aNLb94Vo+KoiQot8F/2RHD21H7/u+wGuKGmlcsYZjT1VTtCqUvMMfxPZWCb738DUB0Y1tCEtyMiramPjQXMADhZGJJtLYrRjjXboU3nsvtDzJ0qXGfsuWGZJLH6SE8vKO72UwRwhhM8YoL8/KApdlOWIIfaDAyIaLtmZ1B2hvQvr4YAP3FW0nKd7E03PGIzIH9IlJ9ELiC0n0p5paKDnYwPMfVbFl/3GSRT+0s/Gc2WELIY6ecMl29NB29L7dDp7iXWzUqqPy27a3SjhrsSEVFXRDrRLJoldMCtnz8jk8OJfngvzk0Y7dbjdIfdMHumG4CViwwNBTBrtrwNDm23BQ7bFRXNz+uYI5wlfAUIhYZYFLCsEp1kIY/ngI9Y/2gGss2HVZmRLHw38tIzu9H8/dexVD0/rBLVf0zPVcQvjCEf0LH1fxn6/tQdMl/eNNLPvyJGxaToiLxkccfSWDvTN+27ZWCSUlsGCxlUFaISNVB9/9QSJZaU3+Y7WlzglGeCZsW/clGyff0jdwUFqxak4ee3gWc+fClc0ODkqbv3vVQopQ0dB0lTyLEcxtC+GTbgf9JmLoSwj+4ixfDg89ZMzYixcb7wdH7MMahHcGzhInFUUVlK8oR9N09k3NoGSmlcnWNFbcfeUXplJlJHzhiP6X6/ai6ZKXv2snNzsNs8mwNtsi9AuZwd5WELWzfttIlrbPIq7WrdQKKzelwVeXhO7THsLdq77yw5FiF45iB9mearKpQkMwrqWCAW9UMFJmoeDBhZnhwoEqNRQkiqLRv8FBe0TfVybdGDqJ8EDXwoWB+hhut9Fzsgckq/74U7OH8/1MfHjrCKpHpzPZZOLF+6/q8ZIklxq+UFe/7dAJTjd7uDN/KFfaBoS81y1C76Q0JxKhR1N5sTs+xc66ocIvKdh14m/0HeYj9+2TZ7GhmFVaXEbxNFWBGj2L57gLDRWTIhl3O5jWbUJ6oleX9JmyMZdDdmxvXUN7X5xgnabvi7hgAWze3CX/aPBz5Is/HRqdTsnNOXjMKldtruWXv5r9hSd5+IIQ/cY1H/DUJ/V8qCVjsySyZO7Ynjt4J6U5bRF6b+unO2MRR7qk4Iki3KL3JT8F9rGy+g+FKOvexFzn4NYCD//5GxuapiJR0JGkDY4n/55coH13UZ/D5dBMpLeuoaMvjk9D6yt05PsidqG2fPhzZP/NTRTfPoKDo9MZdPgc30lK5JZfz7l0vle9jMuW6I+eaebdyiNs+2Qfr9W4gWS+//Er3Ptf95GanNBzJ+qkNKctQr8Q+mk7JdgpBgqAtscY6ZKWLAmdKHzb+Z5NX7173z7HymoofPtfwe1Gr4hjFj+gmBloSEyqoHHFGnZo1ahmldzC3B6/1l7D5dBMpLeuIZovTnA9eR+6sFTzPUe6Jtk3Oo0Xq4/hHjuAf4oz8+D9kxh+bU73r+cywmVF9B5NZ/P+46wpq+XNnfVouiRNeLijciuPFj/L0KYTsHU0XH9tz520kz6Rtgi91/XTnbDi2roku92YLHwPrX2Jvc19bqA4yNcjuZKPWUgRDmHjmnw3ptLoVER9DpdDM5HeuoZ2vzg9OxnaCmycHdSPzQXZ1A9LYVJ6Ir+7exqjMpJ79DyXCy4rov/Xlyt4rbyO5AQThfZhfGN6DqMO7EL8/o+992B2MkrYHqH3qn66E1Zcm5fUzmQRvs8wCmCV96E3xeGUI8nRahluPszse2ezflffyP7sNC6HqHBvXUMXj9vZcEGLpvOGy8Waeyag6pIf5g7le1/LRfmi1DPoAoSMpiXQBca0adPk9u3bO73fzF8X49F13vvBDcSbgmrUXA7Bs+6iJ/yyy5bBT35iTBaqCj//eftSuKD77iQ7ZHLrKxUaY7i46OzX8uCxs3z/b2Xsrj3N7AmD+a87JpCZ0oOu2EsYQohSKeW0SO9dVhZ9fk4a/9hRyy/XfcZ9M4YzJK2f8UafkWtcRPSEFdfZJX/QfbfChVu9QFSTe2z+7wV08qZGu9CUUvJKaQ0/fX0P8SaFv3xrCrMnZvXw4C9fXFZEv+j6EeyqOcWKrYdYsfUQ04cP4LbcIcyblEV6pGSJL9qT3oUJL/QWXSJuiyjMxMtBPNPn0IWbGo3tUHOyif94dTcf7DvGVcMHsPzreWSl9uuVS7hccVkR/djBKbz7gxuobmjitfJa1pTX8pM1u/nvNysZPjAJmyWJhdfYsI+0tPml/KJxf3uIfIsugdVRFGbi5SCeuaDur/Yahfhe78JNbc92kFLyjx21/PT1PehS8l+3T+Cuq4fFfPFdwGVF9D7kWBJ5eNYVPPSlUVTWn+bl7TXUnDzPjuqTvL/3KJ/8+EZSI3wpS7DHrLwgXLJkGIWZeKmLZy5og5K2LPXw15cv79JNjWQ7NDa5+Y81u3lrZz3TbQP4zVdzsQ5I7PFL+6LgsiR6H4QQTBiSyoTbUzl8qpnvPF/KySY3Hk2P+KT3NLFd6quDC0qGPXmzonAxXSpeqLZwQRuUtPVghL/e0NAjN3XL58f54csVHD/r4tHZY/jO9SO/EF2gehOXNdEDOE808X8fHOCV7TVIJH/6xhSj+3uEJ72AniO2y8EHfMHIsAdvVog7o4PiWJeCF6otXNAGJW3N+JFe7+JNlVLy/t6jPLHpINsOnWDkoCSeXngtE4em9uCFfHFxWRN9beN5Zjy2EYAv5w/lX24aHbr8C/tS9iSxXbJujzBcEDLsoZvVl/qt9jbC8zFqsPL8sl6akNt6MHrogak/dZ4fv7qbDXuPMjStHz++dRzfvGoY/cxqxzvHEBU6JHohRAKwCYj3bv+KlPKnQohc4C9Af8ABfFNKeTrC/g7gDEbTIk9bOs/egCXJzI3jMnjv06OMHpwclY+vp4itz/iAO+sSuRj+pqCbpZvi2FmdhqXESQ3WTg2lL/VbvRDwSVTDF0QvLj/SqpF8t9HWg9GNB8bl0Vj1oYM/bNiPR9f58a3juPsaGyZV6eZgYwhHNBa9C/iSlPKsECIO2CKEWAc8DvxQSvmBEOLbwI+An7RxjJlSyuM9M+TokRCn8p0bRvLep0eJC/7yXAAy6xM+4M66RLrhQumKAiTwMdixb9hAY9Ea1qxopPqpY9Su2ECRKKTFo0Q9lL7Ub/VCImRB5JL8+cFKrpOb++yqRtMl63bX88v1e3GeOE/BmEH87PaJ5FhiwdbeQodEL43U2bPeP+O8/yQwBsPSB3gXeJu2if6iQErJWzvrAbh6hLcs8QV0nrdl7Fwwo7mzLpEuulDCXSazl8+mqaGpXdJv/THY8eR4qNY2IjXJAd2KG9GpdoF9rd9qJPSGJDJ49WhSdIZph/xtIvvCqubFdaf49eYKTAkatowE9h4+zelmD2MHJ/PcvdOZccWgizq+LwKi8tELIVSgFBgF/ElK+bEQYjdwO/Aa8BXa7hohgXeEEBJ4Qkr5ZBvnWAQsAsjJ6V7lud++u48VWw5xzu3BV+Hhk0MnmDAk9aI7zy9okLaz/qMu+puCXSYel4e1D61F6rJdizLSx/CtIIt8pOpkq5C0eESnXF99ud9qb8UQgleP4yzH2bO4Hs0t/Kua7kwu3Z2Y/vCPw/ymZAdClbj3p2PpB7MnDmbmmAxunjA4pqa5QIiK6KWUGpAnhEgDXhVCTAS+DfxBCPGfwOuAu43dr5VS1gkhMoB3hRB7pZSbwjfyTgBPglHrpvOXEsCmfccYkGTm29fa6Gc2kZEcz9xJ3nTpi+w8v6DzTGf9R97tG4vW4MCGhex2ej4FEOwyEUIgNdmhRRnpYwi3yO9GuWTlj5HQmzGEwOoxk6mTAvcQ6PLk0hMT06rtnyNUScO6SZzfk8P1P4cl/9S5a4uh++iU6kZK2SiEKAZmSyl/DdwMIIQYDdzaxj513p9HhRCvAtMJuHx6BefdGsMHJvGDm8e0frO3nOdR+mPanWd6w6fTyWCZk2yKVqWguY+hriqK6uEOJuhESyLrF6/v0E/e1scQbJFbuTwI3ocLFUMIvoebl23u8uTSExPTf8ycwuI3PsQyZxcnziRTUJDe6euJofuIRnUzCGjxknw/4Ebgl0KIDC95K8CPMRQ44fsmAYqU8oz395uBn/XsJbSGfaSFZz90cPfKbcwal0mCSSEhTiUhTsXS30z+1VcjepJBOuGP6UoJ4LbQG/7eth7ukhIoKjK2KSxsPbRgcsmYlNHmuILHbLdbLysi7wgXI4bQncmluxOTpkvKzh1CSXQzRLXwzLP9v1Cfd19CNBZ9FrDK66dXgJeklG8KIR4RQjzo3eYfwEoAIcQQ4Gkp5VwgE8PV4zvXi1LK9T19EeH49zljyUxJ4IlNByj+7Fir968eMYB5k4eQlhjHtSMHRi541hG6UeMjopEdfAyXC5YuNf61cRxniZP/LtjAgRYrI+M28OPiWVE1+O5owRDp4S4pMfbxtfxcuRI2bmz7GG35yb9IOve20NsxhPDJvzuTS1f3bdF0nt58iOdKHNSdaube64bz/+aOi/njLyKiUd3sBPIjvP574PcRXq8D5np/Pwhc8D5xCXEqDxSM5N7rhtN43o2rRae5RaO5RWf9nnrWlNXx4zW7ARjY38z1oweRb00jPclM/3gTM64Y1P6XsodqfITA59NxuYzu2++9ZzRNDrLsg4m6vKiBFe5voqHygVsjr2gPD7TzIEa7YIj0cD+/DFpaAtt0NbbwRdO5dwY9sTorfbKUtQ+uRdd1TPEm/0Tancmls/s2Nrl58MUdbN3fwHWjBvLT2ydw8/hMvMZeDBcJl3VmrNmkkBHWH3ZSdio/vHkMzhPncTSc4/mPqti07xj/2FHr32bOxMH8752T2rb0e6PGh8+ns3SpQfK6HsKo4US94JaRaBjNtjUkDmztWuydWXSEP9wFBRAXF7DouzqXfRF17tGsonpipeMscbL2obXoHh0Aj8vTaiINn0y6PLlEuKizLg8vflzF05sPcbLJza/+aTJfmRabxPsKLmuibwtCCHIsieRYErl+9CCklNSdaqbJ5eG18jr+8sEBPnGc4Od3TGSOT60TjB6s8RECu90g+s2bW60Owom6/+Bkro/byjUtH/Bh3A2MzL+2XYu9O2IjX/2q9nz00LFVeiF81H2pkFy0q6ieWOk4ih1ILSBWUxQlZCKNlOsQHDSPZnIpfbKUymc+ZPyOF5gqt4PZTMPad3nWPZBVHzo43ezh2lEWfnTLWPKsaZ0afwy9iy8k0YdDCMFQbzeqH94yhlsnZ/GjVyp44IUdzJ00mP+6fSKDkuMDO/Rm2msbxw4n6u/llzBeuQkh3EjFzAtlG3C77W1a7N0dckfzmLPEyaqCVWgtGmqcysLihW2SfQ1Wni+GAnr21vW1QnLtraKCJ8XurHR8E1t842ikugnQUFTB3D/ODbn/4ZNJ5erKTk0upU+W8uZ33gTgILM5lpzIR1eN5m/rT9BMI7dMyOSBglExgu+jiBF9BIzLSmHN967liU0H+f17n1Ny4AOW3j6B23OHBHyNvVntK8Kxw4l6UnExeNwgNfC4uYFizGZ7uxZ7bw65oqgCza0BoLk1KooqIhJHb5JxXysk19YqKpKrJtqVTvAEUYOVWbN8YZ1MMsV9jFf38aM/2pi6KPQY4ZPJ+AXjqd5cHTK5tLciq1xdiQSODUnis7yBrBo/FQWdO6wJPPCVqxmVkdyzNy+GHkWM6NuASVV4cOYobpmQyY9e2ckjfyvnF+v2kjMgkX5mlTvzh6LpkpqT55k2LJ1rRg3s9TGFEnVBCIsMKyxgQ2HfcVu0hd4k4z5TSM6LtlZRkVw1M5bM6NB1Ej5BsLAQt9uKbrjlOSIzOS4zuakB5oTtG8lt5pPBNlhGU1TkRl9ZhPS0duUcOn6OnTOtrLclciY9AbVFY/bp4/y/r15B9qzrevSexdA7iBF9BxiVkcwr372GV0qdfHiggfrGZnbWnGol2/zmVTn8z52TeuScUfmZI7CInYtH8LmFuZSvLPeTUG5hZLFVb5JxnygkF4ZIq6iuumoqiirwNHtAGqumkTgwm61+oZaitH9Pw4PsNVh5sdrKyv+C6e7NzJQaCpKWZo3/396ZB8dR3Xn88+bQjKTRSLJuS9ZlGfkSlvERG7CxjTGOIctNwSaBxLWVsAFCNkvCkXKKLGwIuAipLZZQCQuJUht2nVBkveADstgGggnBloVtbFmyZFvWPZZ1a0Zz9P4xo6mRPZKlkTQzGv8+VV3q6Z7X/X1Pv/l19++9/r1dv6snTnPzVmUjhxo6UQrKZlopOd7BbdcVs+qBvwutQYSIoDRtQtkGpoSlS5dqn332WaRljEiP3cmhhk5SE+Iw6BUbf/EhAFvvvJLiDAu5KfFkJ5svcZTgRFuc+UJGuwiNdRRHNHWYjosJCg9sH2BcoZqEtAR2PrzTHx7Tm/Tcv+d+fyrntDTv4K/ytAbclVWA9+I7UvisosL7PsTgIGga5NHA11UFfakGzhancuD6XADm51i5bXEuX1k0M2SbFsKDUurASGngxdFPAofPdvHg7w9ypqPfv81iMvDT28t4Ze9JPJrGdVdkoAFJJgNr52aOOHPOs8/Cli3e0IZeD08/DZeYKClsRPtFaEqZYOXHMoQy2PDHoTJKp/C4PeABFCz59hJu/uXNw8of+NUB3vnOO/7RN0qvuOnlm1jyrSUXVcPudGMusBE/uw2DdQBDSj+G5H6UwVvWfiqNuxdcwb9tmRFigwnhZjRHL6GbSaAsL5l9P1jDF83dtPU4qG/v41/e/oLvvlHp/069rQ+lwO708MJ7JyhKT2TPo2suOla0xZkDibbOzrAywcoHi8sPbR8p+VhgGTTvkElNaReFxhr2N1BVUcXBXx8cNsRSc2vseHAHmWWZ5K3I4/S5fl7ebcOy0UZ6gQ2dyYXHYcDVmYDTlkR/bRZOm4XB1mRUVxL3PiIvOcUK4ugnCf9E5MDaUthUlsPrH9fT1u3gq1/KZ2mh986o3tbHd9+o5HBjFy+9X8ND6+YMO85QnLmmYj/XsZcC7yDEsNcnGNF8EZowlwrLTLDyF8blE9IShg1HLd9cjtvh9mb9dLj9F4DAMsHy/A/d9Q/F7gPpTTLSUmjlyR3HqNtXQ2PnAACmHDMD1Tk4TmZz77p0vvEt76Q8FRXQ0gLZ1478roQwPZHQTQRwuj18f1sV/1vVxDeuLuTHN89HF5hyIYpjJNM2vj4aY23vSYzRV1VUceCVA/59+avzOfPBGf/na354DeufW3/Jfo8Pn/2QPVu8k7U4THoai5NpLkiiOT+JnlRvTN1q1HNNaQYrZ6dxbUk6LTWJ7NunYut/KEjoJtow6nVsvfNKTrb18puPT/GXWhu3Ls7lziV5ZFnNsHcvLXEWPigppzqzkORdX5Cs5VCUnsjVs9MiOqfmaGPxpyKbZlgYa1hmgi8iBI56qaqoGrbPZXeBwn9Xvv/F/ZTeWho014ymaThcHhwuD/HLZ1KzOIO64mSa8q1oeoXFoKM83UJBp4P1K/K5bsOcYTcSxRlw9dUhV0OYhoijjxBmo563H76WNw+e5Qd//Jytu6vZuruaDfOzqLQvov2B1wGIczsZdBhh+1F/2T2PrqEoPTFS0oMyrTNTRiAmlbN4eGqNojVFtBxs8eeq0dxa0LdVv/n6p+y5MCPr9flk6hT3FKdz14Y5LMpLkUyRwjDE0UcQnU5x19JZ3HFVHh/V2th1tIXf/9X7+D7Pqucn7mqWXr8M1/IVdNudvPR+LRX7T3H7y39h5yOro2q427TOTBmBAfj95/q9Sb89gA5MKSY2/fsm7zSMbg2dQUfTp028/Y9vM/erZbTlJLL9UBN7qtvJspq4b2UhJoMOk1HPVfkpzM+xSoZIYUQkRh9luD0ard12cpLNQX+4e6rb+ObrfwPgxgVZzM9JZtGsZBblpYSWV3+SmNZ39GFmaJTModcO4XF70Ol1lG8u94+kqayoYveuEzTNtNAyK4m2PAtug444g467luSx5eb5mI36CNdCiDZkHH2Mse1vDbz7RSv1tl7qbH3+CdBLMi0UzEhgRXEaf/+lfBJN4X1gm7Yx+jASeEHUGXSUfLmE2p21eFwe+tPMJPxsHdtPtGHTNNA0ZrQNkN3Qw42rC9n8w1UkmY2RroIQpUhnbIxx97JZ3L3M60h77E4ON3ZReaaTyjPnqbf18X/H2/jXHcf46W1l7DzSzJOb5jEvxzrlukKe4CImh/IEJzDE5cGDo99JXZGV44syaCpIQlW3siTDwsKKI8ysPo/J4X1Cuv3pG8TJCyEjd/QxyKsf1vHMO8eGbVs1J51nbl1IQVp0deJG01DScDyR+Me9D7qpL0vn2E3FtLrcWLoclB7r4PEta1l2/Wx/eAdGTmUgCIHIHf1lxj+sKuaW8lw6+gZp6hrg4OnzVOw/zf2vfcqu762OrvhulLxuG64+hlkrZ7H4D3fwwsenOOl2U5Zl4aGZKeRXd1B83zL/Oad6blnh8kIcfYySkWQiI8lEaXYSa0szSY438sw7x1j/83189Ni6Ucv2OlwcOtPJoNtNltVMakIcSWYDjZ0DdPQN0tXvpH/Qjd3lnYfX5fZQ29ZLSoKRkkwLGxfkkJwwxjBDlLxue+Goofo99XiumEGP3cX8HOvwF9pC5FBDJy++d4J9J9rJTDLxwsaF3LY4d1KOLQijIY7+MmFo5p+z5wdwuT1BX7pq73Fw768/obatd9zHT443Yne6cbg8PPbmYbZ9eyXLi8aQECtKcgunXTOL5pIUWtPNtOdaeNPeT9fWvQBkW81sXJjNjQuyWV40I6Qx6h/V2Pjaf/yVhDg9j395LvetLCAhTn5+QniQGP1lwvvHW9n8G2+bLi+cwSPr57BgppWUhDhcbg87jrT4k7AtK0zl4XVzsJgNtHbZOdc3SK/DRV5qPGmJJpLjjVhMBsxGHTqd4uz5ARbOtKLXKYqe2OE/56aybMCb6nbQ5UEpRZxBh1GvMOp1vnUdMxLiyLKaKUhPwDrFHY52p5ua1l6OtXRzvLmHmrYeGjsHqGvvA7wvpxYkmVhemkH5rFTiDDrePdrCvhPtOFwe0hLjWD8vizWlGaydm3nJMNj+k+f4U2Uj2w40oGmw+3urKc2W2ZiEyUeGVwoAtHbb+cWfT7Dts7O4Pd7/e3K8ka4Bp/87P797EbdflRfyOZ7afpQPTrSj1ylqAp4MlIKxmFpGkolZqfGUz0qlLM/KvBwrBp1idoblki8E2Z1ujjZ1cfpcP32DbtIT4zjW3E233UVT5wAn23upt/XhqzrxRj1XZFnIspq5Mi+ZxfmplOUlB73Y9A+62Fvdzq4jLew53kaPw4XFZGDD/Cy+Uj6Ta0vSUcCTbx2mtq2XJQWpHGnsZn/dOZJMBlaXZnDHVbmsm5s1nuYUhDEjjl4YRmf/IG8ebOTg6fO8c7iZOIOO799wBV9fUTAlY+/tTjdGvQ6dAqdbw+n24HR7GHR7cDg9dPQN0txlp87WyylbH6ds/Xze2Ind6Rl2nHVzM/nainy6Bpw8t7OavNR4WrrtmI16OvoG6egbvOjcOgWJJgNZVjPF6YnMzbEyLzuJeTlW8mckhBQfd7k9fFLXwfaqRnYeaaHH7iI1wUiS2eifkyDOoMNqNvLQ2tncszw/ujrAhZhEHL0wIg6XG71SEU2UFgyX20OdrY/KM+f53SenKUq3cPD0eX+qXYCFuVbyUhLodbjISTZjNuqxmA3cuCCbli47uSnxlGRaiI+bOifrcLn54ISN7VVNVJ45z8YF2fzzhlKMeoVSSnLOCGFDHL0QEwy6PLz6UR1HGrvYfE2RP8e/IAgyjl6IEeIMOr6zpiTSMgRh2hFdz+uCIAjCpCOOXhAEIcYRRy8IghDjiKMXBEGIccTRC4IgxDji6AVBEGIccfSCIAgxjjh6QRCEGCcq34xVSrUDp8NwqnTAFobzTCbTUTOI7nAyHTWD6J4oBZqmZQTbEZWOPlwopT4b6ZXhaGU6agbRHU6mo2YQ3VOJhG4EQRBiHHH0giAIMc7l7uh/FWkBITAdNYPoDifTUTOI7injso7RC4IgXA5c7nf0giAIMY84ekEQhBgn5hy9Uuq/lVKHfMsppdQh3/ZCpdRAwL5XRij/lFKqMeB7mwL2PaGUqlVKVSulbowy3VuVUseVUp8rpd5SSqWMp3wEdc9QSr2nlKrx/U0N2Dcl7T2S5oD9+UqpXqXUo1NR5wjqjirbHofusNv2JGgOu12PiqZpMbsALwA/9q0XAkfGUOYp4NEg2+cDVYAJKAJOAvoo0r0BMPjWnwOeG0/5COp+Hnjct/54gO6wtHeg5oBtbwJ/CGYHk1HnSOmONtseh+6I2naImiNq1xcuMXdHP4RSSgF3A29M0iFvAf5L0zSHpmn1QC2wfJKO7SdU3Zqmvatpmsv38RMgb7K1jcYE2vsW4Le+9d8CtwZsn9L2DqZZKXUrUAccDaV8OJio7iBEzLbHojuStj2Bto6YXQcjZh09sApo1TStJmBbkVKqUim1Tym1apSyD/keE18LeOTKBRoCvnPWt22ymYjuITYDOydQPhRC1Z2laVozgO9vpm97ONp7mGalVCLwGPCTUMr7CHtbj1N31Nh2CO0N4bftUDVH0q4vYlpODq6U+jOQHWTXjzRN+x/f+r0Mv9NqBvI1TTunlFoC/EkptUDTtO4LjvFL4GlA8/19Aa9xqSDnG9fY1CnWPXSOHwEu4D9DKR8p3cFOG2TbmNs7RM0/AV7UNK3XeyN3SSa7zlOtO9pse1ztPdm2HSYbuei0QbZN/Rj3qY4NRWLBewFrBfJG+c5eYOkljlOILwYIPAE8EbBvN7AymnQD9wP7gYSJ1DucuoFqIMe3ngNUh6O9g2kGPgRO+ZZOoAN4aCptLNy6A8pE3LbH2d5ht+2JaI6UXY9Yl6k+QSQWYCOw74JtGfg6PYBioBGYEaRsTsD6P+GNpwEsYHgnSh2T3IkyQd0bgS+AjFDKR1D3VoZ3Wj0fjvYOpvmC/U8xSmfsROocKd3RZtvj0B0R256g5ojY9UhLrMbo7+HiDrLVwOdKqSrgj8ADmqZ1ACilXlVKDWWfe14pdVgp9TmwFu8PAk3TjgLb8BrcLuBBTdPcUaT7JSAJeO+CoWYjlo8S3T8DblBK1QA3+D6Ho72DaR6RCzSPVD5SbT0iUW7bIxIltj0RzZGy6+DafFcZQRAEIUaJ1Tt6QRAEwYc4ekEQhBhHHL0gCEKMI45eEAQhxhFHLwiCEOOIoxcEQYhxxNELgiDEOP8PbRFk8kqwUDoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ALL TRIMS COMPLETE. let's convert the partisan data to the format we use for HD code\n",
    "# in this prototype, we only use 2020 presidential data, but this could be modified\n",
    "vtdGOP = [0.]*nPrecincts  #Each precinct's R/(R+D)\n",
    "vtdPop = [0.]*nPrecincts   #THIS IS VOTER POPULATION\n",
    "vtdArea = [0.]*nPrecincts\n",
    "vtdCPx = [0.]*nPrecincts   #these are centroid x and y of each precinct\n",
    "vtdCPy = [0.]*nPrecincts\n",
    "plotcolor = ['blue']*nPrecincts\n",
    "for p in range(nPrecincts):\n",
    "    vtdPop[p] = vtdTrump[p] + vtdBiden[p] \n",
    "    vtdGOP[p] = vtdTrump[p]/max((vtdTrump[p] + vtdBiden[p]), 0.01)  #avoid divide by zero\n",
    "    if (vtdGOP[p] > 0.40) :\n",
    "        plotcolor[p]='purple'\n",
    "        if (vtdGOP[p] > 0.60) :\n",
    "            plotcolor[p] = 'red'\n",
    "    vtdArea[p] = vtdGeom[p].area\n",
    "    vtdCPx[p] = vtdGeom[p].centroid.x  #not needed except for graphing\n",
    "    vtdCPy[p] = vtdGeom[p].centroid.y   #not needed  \"  \"  \"\n",
    "    if isSkippedPrecinct[p] == 0 and p%10 == 0: #memory drain if we print all\n",
    "        plt.scatter(vtdCPx[p], vtdCPy[p], marker='.',c=plotcolor[p])\n",
    "for p in range (nPrecincts):\n",
    "    if p%100 == 0:\n",
    "        print(p,vtdGOP[p],vtdPop[p])\n",
    "plotPoly(tractMAP)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "a1e3f0d2-c21c-46d4-8227-e75dfd5e89b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "original and final map areas are 2.145382549685004 2.0378079955546173\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "MAP = tractMAP  #committing to the trimmed map (for TN, using convex hull)\n",
    "#bufferDistance = 0.02  #do minor buffer for OH\n",
    "#MAPbuffer = MAP.exterior.buffer(bufferDistance, single_sided=True)  #gives a little exterior buffer\n",
    "# MAP = origMAP.convex_hull  #alternate to buffer for weird state shapes\n",
    "bBox = MAP.bounds\n",
    "MAPMinX = bBox[0]  #these four are useful for slicing ops, and are basis for map grid (without buffer)\n",
    "MAPMinY = bBox[1]\n",
    "MAPMaxX = bBox[2]\n",
    "MAPMaxY = bBox[3]\n",
    "\n",
    "#MAP = MAP.union(MAPbuffer)  #not buffering for TN\n",
    "print(\"original and final map areas are\",wholeMAP.area,MAP.area)  #same if we didn't buffer\n",
    "plotPoly(tractMAP)\n",
    "plotPoly(wholeMAP) \n",
    "plt.show()\n",
    "\n",
    "minTractPop = 10  #was zero to not exclude empty interiors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "8e271f55-e118-4575-bc99-c28c59f435b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020 population, Trump+Biden voters, lean =  9288982 4491609.0 0.4192871641320516\n",
      "for comparison, uselectionatlas has census, Trump + Biden = 9288994 4491713.0 0.4192861387181238\n"
     ]
    }
   ],
   "source": [
    "#let's confirm some population stats - these match 2020 census, USelectionatlas.org :-)\n",
    "censusPop = 9288994\n",
    "atlasTrump = 1883313.\n",
    "atlasBiden = 2608400\n",
    "print(\"2020 population, Trump+Biden voters, lean = \",np.sum(tractPop),np.sum(vtdPop),stateGOP )\n",
    "print(\"for comparison, uselectionatlas has census, Trump + Biden =\",censusPop,atlasTrump+atlasBiden,\n",
    "      atlasTrump/(atlasTrump+atlasBiden) )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "6c47f717-f208-4a4c-b825-a3181a83c6ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "state pop before, after slice operation are 9288982 9288974.0\n",
      "state Trumpers before, after slice opn are 1883274.0 1883273.9999999942\n",
      "state Bideners before, after slice opn are 2608335.0 2608335.000000007\n",
      "missed pop for tract, pop, (x,y) 1995 6 -74.09628111961266 39.82913826197979\n",
      "missed pop for tract, pop, (x,y) 1996 2 -74.32342735680838 39.53918928965329\n"
     ]
    }
   ],
   "source": [
    "#Check on integrity after slicing\n",
    "sumPop = 0.\n",
    "sumTrump = 0.\n",
    "sumBiden = 0.\n",
    "for t in range(nTracts):\n",
    "    if isSkippedTract[t] == 0:\n",
    "        sumPop += tractPop[t]\n",
    "for p in range(nPrecincts):\n",
    "    if isSkippedPrecinct[p] == 0:\n",
    "        sumTrump += vtdTrump[p]\n",
    "        sumBiden += vtdBiden[p]\n",
    "print(\"state pop before, after slice operation are\",np.sum(tractPop),sumPop)\n",
    "print(\"state Trumpers before, after slice opn are\",np.sum(vtdTrump),sumTrump)\n",
    "print(\"state Bideners before, after slice opn are\",np.sum(vtdBiden),sumBiden)\n",
    "for t in range(nTracts):\n",
    "    if isSkippedTract[t] ==1 and tractPop[t] > 0:  #should not occur\n",
    "        print(\"missed pop for tract, pop, (x,y)\",t,tractPop[t],tractGeom[t].centroid.x,\n",
    "              tractGeom[t].centroid.y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "2f6c72cd-90d8-489d-9b15-d4278ba17b3d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "map has a total of 63 grids for 12 districts\n",
      "starting grid no 0 at x = -75.44057042857142, y = 39.28401594444445 \n",
      "starting grid no 5 at x = -75.44057042857142, y = 40.50377538888888 \n",
      "starting grid no 10 at x = -75.20257928571428, y = 39.52796783333333 \n",
      "starting grid no 15 at x = -75.2025792857143, y = 40.74772727777778 \n",
      "starting grid no 20 at x = -74.96458814285714, y = 39.77191972222222 \n",
      "starting grid no 25 at x = -74.96458814285714, y = 40.99167916666667 \n",
      "starting grid no 30 at x = -74.72659700000001, y = 40.01587161111111 \n",
      "starting grid no 35 at x = -74.72659700000001, y = 41.23563105555556 \n",
      "starting grid no 40 at x = -74.48860585714284, y = 40.2598235 \n",
      "starting grid no 45 at x = -74.2506147142857, y = 39.28401594444445 \n",
      "starting grid no 50 at x = -74.2506147142857, y = 40.50377538888888 \n",
      "starting grid no 55 at x = -74.01262357142858, y = 39.52796783333333 \n",
      "starting grid no 60 at x = -74.01262357142858, y = 40.74772727777778 \n",
      "done building grids. avgGridDensity, maxGridDensity,nPopulatedGrids = 3137133.248158339 24436430.51275278 51.0\n"
     ]
    }
   ],
   "source": [
    "# this section - ESTIMATE POP'N DENSITY at scale of 1/3 of district length\n",
    "# also, IDENTIFY WHICH TRACTS and VTD's INTERSECT EACH GRID to speed later intersection searches by grid \n",
    "# (above sections already pulled in Tract and Precinct geometry and demographic data, built an overall map)\n",
    "nDistricts = 12   #NJ congressional\n",
    "avgDistrictPop = np.sum(tractPop) / float(nDistricts)\n",
    "#MAPMaxX = -80.   #In most state maps, we figure this out from the bounding box of the convex hull; see above.\n",
    "#MAPMaxY = 31.1   # ... but for Florida, we will do manually\n",
    "#MAPMinX = -87.0\n",
    "#MAPMinY = 25.5\n",
    "stateWidth = float(MAPMaxX - MAPMinX)\n",
    "stateHeight = float(MAPMaxY - MAPMinY)\n",
    "stateWHRatio = stateWidth / stateHeight\n",
    "G = 2.5  #adjustable parameter; G*G = number of grids that fit in an average district\n",
    "nGridsX = int( G*round((nDistricts*stateWHRatio)**0.5,0) )   #OK to have empty grids for a non-rectglr state\n",
    "nGridsY = int(round(nGridsX / stateWHRatio,0) )  #so grids are square even if state has high aspect ratio\n",
    "nGrids = int(nGridsX*nGridsY)\n",
    "print(\"map has a total of {0} grids for {1} districts\".format(nGrids,nDistricts) )\n",
    "nGridPrecincts = [0]*nGrids # will store how many precincts intersect with each grid square\n",
    "nGridTracts = [0]*nGrids    # same, but for tracts\n",
    "gridPrecinctNo = [[0]*nPrecincts for gridNo in range(nGrids) ] # initialize a list of VTDs that intersect with each grid square\n",
    "gridTractNo = [[0]*nTracts for gridNo in range(nGrids) ] # initialize a list of tracts that intersect with each grid square\n",
    "\n",
    "gridPop = [0.]*nGrids #will store approx total population for this grid square\n",
    "gridDensity = [0.]*nGrids\n",
    "gridGeom = [Polygon([(0,0),(0,1),(1,0)])]*nGrids  #initialize grid geometry\n",
    "gridWidth = stateWidth /nGridsX        #geo length of a grid's side length\n",
    "gridHeight = stateHeight /nGridsY    \n",
    "\n",
    "for nG in range (nGrids) : #  create polygon shape for each grid square, compute grid-square population density\n",
    "    x = int(nG/nGridsY)\n",
    "    y = int(nG % nGridsY)\n",
    "    point1 = Point(x*gridWidth+MAPMinX,y*gridHeight+MAPMinY)\n",
    "    point2 = Point((x+1)*gridWidth+MAPMinX, y*gridHeight+MAPMinY)\n",
    "    point3 = Point((x+1)*gridWidth+MAPMinX, (y+1)*gridHeight+MAPMinY)\n",
    "    point4 = Point(x*gridWidth+MAPMinX, (y+1)*gridHeight+MAPMinY)\n",
    "    gridGeom[nG] = Polygon([point1, point2, point3, point4])\n",
    "    if (nG %5 == 0): #print occasionally, should take about one second per grid\n",
    "        print(\"starting grid no {0} at x = {1}, y = {2} \".format(nG,gridGeom[nG].centroid.x,gridGeom[nG].centroid.y) )\n",
    "    counter = 0\n",
    "    #    Now for each grid square, find intersxn with all polygons, total the intersection population\n",
    "    #    Also, create 2 lists: of TRACTS and precincts that intersect each grid (efficiency shortcut)\n",
    "    \n",
    "    if( gridGeom[nG].intersection(MAP).area > 0.0001) : #don't bother w grids off the map\n",
    "        for t in range(nTracts):\n",
    "            # if (t%3000 == 0) :\n",
    "            #    print (\"grid {0}, tract no {1}, ({2},{3})\".format(nG,t,tractGeom[t].centroid.x,tractGeom[t].centroid.y) )\n",
    "            intersxnArea = 0.\n",
    "            if (notPoly[t] == 0) :  # tract is a simple polygon\n",
    "                intersxnArea = gridGeom[nG].intersection(tractGeom[t]).area  #replaced tractGeom w cleanedPoly\n",
    "            else : #tract is a multiPolygon, do the polygon intersections individually\n",
    "                #print(\"I think tract\",t,\"is a multiPolygon\")\n",
    "                for geom in tractGeom[t].geoms :\n",
    "                    intersxnArea += gridGeom[nG].intersection(geom).area\n",
    "            if (intersxnArea > 0 and tractPop[t] > minTractPop) : # this tract is at least partially in this grid and populated \n",
    "                if (tractArea[t] == 0):  #should not happen, but debugging\n",
    "                    print(t,tractGeom[t].centroid.x, tractGeom[t].centroid.y)  #debug print\n",
    "                gridPop[nG] += tractPop[t]*intersxnArea/tractArea[t]\n",
    "                if tractPop[t] > (1.0 * avgDistrictPop) :  #flag if we have a mega tract\n",
    "                    uhoh = input(\"Uh oh! We have a tract that's bigger than a district.  What now?\")\n",
    "                else :\n",
    "                    gridTractNo[nG][counter] = t  #add this tract to this grid's list, update the no of tracts in grid\n",
    "                    counter +=1            \n",
    "                    nGridTracts[nG] = counter\n",
    "        # OK, now, loop over PRECINCTS to develop each grid's list of precincts (prev loop was for tracts)\n",
    "        counter = 0\n",
    "        for p in range(nPrecincts):\n",
    "            intersxnArea = 0.\n",
    "            if (notPolyVTD[p] == 0) :  # precinct is a simple polygon\n",
    "                intersxnArea = gridGeom[nG].intersection(vtdGeom[p]).area  #replaced tractGeom w cleanedPoly\n",
    "            else : #precinct is a multiPolygon, do the polygon intersections individually\n",
    "                for geom in vtdGeom[p].geoms :\n",
    "                    intersxnArea += gridGeom[nG].intersection(geom).area\n",
    "            intersxnArea = gridGeom[nG].intersection(vtdGeom[p]).area\n",
    "            if (intersxnArea > 0 and vtdPop[p] >0) : # this precinct is at least partially in this grid and recorded votes                 \n",
    "                gridPrecinctNo[nG][counter] = p  #add this precinct to this grid's list, update the no of tracts in grid\n",
    "                counter +=1            \n",
    "                nGridPrecincts[nG] = counter\n",
    "    gridDensity[nG] = gridPop[nG] / gridGeom[nG].area  #finished loop over tracts & vtd's.  Calceach grid's population density    \n",
    "\n",
    "minGridPop = np.min(gridPop)\n",
    "# avgGridPop = np.mean(gridPop)   #see below for explicit averaging, since there are so many empty grids\n",
    "maxGridPop = np.max(gridPop)\n",
    "nPopulatedGrids = 0.\n",
    "totPopulatedGridArea = 0.\n",
    "for nG in range(nGrids) :\n",
    "    if gridPop[nG] > 0. :\n",
    "        nPopulatedGrids +=1\n",
    "        totPopulatedGridArea += gridGeom[nG].area\n",
    "avgGridPop = np.sum(tractPop) / nPopulatedGrids\n",
    "avgGridDensity = np.sum(tractPop) / totPopulatedGridArea\n",
    "#avgGridDensity = np.average(gridDensity) #avgGridPop / (gridPL * gridPL) #these densities help home district algorithm\n",
    "maxGridDensity = np.max(gridDensity)\n",
    "print(\"done building grids. avgGridDensity, maxGridDensity,nPopulatedGrids =\",avgGridDensity,maxGridDensity,nPopulatedGrids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "808f513f-21af-4b03-8061-fdeeed33dafd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is a heat map of grid density \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"here is a heat map of grid density \")\n",
    "xyGridDensity = [[0.]*nGridsX for x in range (nGridsY) ]   #flipping x and y to get right orientation in pixel grid\n",
    "for nG in range (nGrids) : #  create polygon shape for each grid square, compute grid-square population density\n",
    "    x = int(nG % nGridsY)\n",
    "    y = int(nG / nGridsY)\n",
    "    if gridDensity[nG] > 0 :  #avoid log zero\n",
    "        xyGridDensity[x][y]=np.log(gridDensity[nG])\n",
    "c=plt.pcolormesh(xyGridDensity)\n",
    "plt.colorbar(c)\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "4013f80b-4fc8-40a1-b708-ee29c29b3ffe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.3142802343958521e-20 6.931243500010116e-06\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(np.min(vtdArea), np.min(tractArea))\n",
    "minTractPop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "ab450500-f1a4-4292-a2fe-71eb2217121d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I am working on tract number 0 of 2181 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 0\n",
      "we have 2 non-opposing shorted wedges for tract no 1\n",
      "I am working on tract number 20 of 2181 tracts\n",
      "I am working on tract number 40 of 2181 tracts\n",
      "I am working on tract number 60 of 2181 tracts\n",
      "I am working on tract number 80 of 2181 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 91\n",
      "I am working on tract number 100 of 2181 tracts\n",
      "I am working on tract number 120 of 2181 tracts\n",
      "I am working on tract number 140 of 2181 tracts\n",
      "I am working on tract number 160 of 2181 tracts\n",
      "I am working on tract number 180 of 2181 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 188\n",
      "we have 2 non-opposing shorted wedges for tract no 194\n",
      "we have 2 non-opposing shorted wedges for tract no 195\n",
      "I am working on tract number 200 of 2181 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 207\n",
      "7 yoyos for tract,wedge,wedgePop,r= 211 2 792750.9284010406 0.9062\n",
      "7 yoyos for tract,wedge,wedgePop,r= 211 2 447279.23366241495 0.4531\n",
      "8 yoyos for tract,wedge,wedgePop,r= 211 2 82987.05721437652 0.2265\n",
      "9 yoyos for tract,wedge,wedgePop,r= 211 2 343438.45855260995 0.4143\n",
      "10 yoyos for tract,wedge,wedgePop,r= 211 2 91121.08733821724 0.3204\n",
      "10 yoyos for tract,wedge,wedgePop,r= 211 2 149416.08760080917 0.3673\n",
      "11 yoyos for tract,wedge,wedgePop,r= 211 2 253658.0804296041 0.3908\n",
      "11 yoyos for tract,wedge,wedgePop,r= 211 2 201129.8879284498 0.3791\n",
      "12 yoyos for tract,wedge,wedgePop,r= 211 2 177186.2754121254 0.3732\n",
      "I am working on tract number 220 of 2181 tracts\n",
      "I am working on tract number 240 of 2181 tracts\n",
      "I am working on tract number 260 of 2181 tracts\n",
      "I am working on tract number 280 of 2181 tracts\n",
      "I am working on tract number 300 of 2181 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 300\n",
      "we have 2 non-opposing shorted wedges for tract no 301\n",
      "we have 2 non-opposing shorted wedges for tract no 303\n",
      "we have 2 non-opposing shorted wedges for tract no 304\n",
      "we have 2 non-opposing shorted wedges for tract no 306\n",
      "we have 2 non-opposing shorted wedges for tract no 307\n",
      "I am working on tract number 320 of 2181 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 335\n",
      "I am working on tract number 340 of 2181 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 347\n",
      "I am working on tract number 360 of 2181 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 366\n",
      "I am working on tract number 380 of 2181 tracts\n",
      "I am working on tract number 400 of 2181 tracts\n",
      "I am working on tract number 420 of 2181 tracts\n",
      "I am working on tract number 440 of 2181 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 444\n",
      "we have 2 non-opposing shorted wedges for tract no 445\n",
      "I am working on tract number 460 of 2181 tracts\n",
      "I am working on tract number 480 of 2181 tracts\n",
      "I am working on tract number 500 of 2181 tracts\n",
      "I am working on tract number 520 of 2181 tracts\n",
      "I am working on tract number 540 of 2181 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 551\n",
      "I am working on tract number 560 of 2181 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 573 3.0 0 90.0 0.3953 220823.1\n",
      "I am working on tract number 580 of 2181 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 581\n",
      "we have 2 non-opposing shorted wedges for tract no 594\n",
      "we have 2 non-opposing shorted wedges for tract no 597\n",
      "I am working on tract number 600 of 2181 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 608\n",
      "I am working on tract number 620 of 2181 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 627\n",
      "we have 2 non-opposing shorted wedges for tract no 636\n",
      "we have 2 non-opposing shorted wedges for tract no 637\n",
      "we have 2 non-opposing shorted wedges for tract no 638\n",
      "I am working on tract number 640 of 2181 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 655\n",
      "I am working on tract number 660 of 2181 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 662\n",
      "I am working on tract number 680 of 2181 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 689\n",
      "I am working on tract number 700 of 2181 tracts\n",
      "I am working on tract number 720 of 2181 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 720\n",
      "I am working on tract number 740 of 2181 tracts\n",
      "I am working on tract number 760 of 2181 tracts\n",
      "I am working on tract number 780 of 2181 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 789\n",
      "I am working on tract number 800 of 2181 tracts\n",
      "I am working on tract number 820 of 2181 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 820\n",
      "we have 2 non-opposing shorted wedges for tract no 821\n",
      "I am working on tract number 840 of 2181 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 845\n",
      "I am working on tract number 860 of 2181 tracts\n",
      "I am working on tract number 880 of 2181 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 889\n",
      "we have 2 non-opposing shorted wedges for tract no 893\n",
      "we have 2 non-opposing shorted wedges for tract no 894\n",
      "we have 2 non-opposing shorted wedges for tract no 897\n",
      "I am working on tract number 900 of 2181 tracts\n",
      "I am working on tract number 920 of 2181 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 925\n",
      "I am working on tract number 940 of 2181 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 940\n",
      "I am working on tract number 960 of 2181 tracts\n",
      "I am working on tract number 980 of 2181 tracts\n",
      "I am working on tract number 1000 of 2181 tracts\n",
      "I am working on tract number 1020 of 2181 tracts\n",
      "I am working on tract number 1040 of 2181 tracts\n",
      "I am working on tract number 1060 of 2181 tracts\n",
      "I am working on tract number 1080 of 2181 tracts\n",
      "I am working on tract number 1100 of 2181 tracts\n",
      "I am working on tract number 1120 of 2181 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1134\n",
      "I am working on tract number 1140 of 2181 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1152\n",
      "we have 2 non-opposing shorted wedges for tract no 1158\n",
      "I am working on tract number 1160 of 2181 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1179\n",
      "I am working on tract number 1180 of 2181 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1183\n",
      "we have 2 non-opposing shorted wedges for tract no 1185\n",
      "we have 2 non-opposing shorted wedges for tract no 1186\n",
      "I am working on tract number 1200 of 2181 tracts\n",
      "I am working on tract number 1220 of 2181 tracts\n",
      "I am working on tract number 1240 of 2181 tracts\n",
      "I am working on tract number 1260 of 2181 tracts\n",
      "I am working on tract number 1280 of 2181 tracts\n",
      "I am working on tract number 1300 of 2181 tracts\n",
      "I am working on tract number 1320 of 2181 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1327 5.0 3 90.0 0.5529 212336.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1327 6.0 3 90.0 0.5153 212336.9\n",
      "I am working on tract number 1340 of 2181 tracts\n",
      "I am working on tract number 1360 of 2181 tracts\n",
      "I am working on tract number 1380 of 2181 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1392 2.0 1 90.0 0.2105 242880.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1392 3.0 1 90.0 0.1766 242880.3\n",
      "I am working on tract number 1400 of 2181 tracts\n",
      "I am working on tract number 1420 of 2181 tracts\n",
      "I am working on tract number 1440 of 2181 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1448\n",
      "I am working on tract number 1460 of 2181 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1464\n",
      "I am working on tract number 1480 of 2181 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1481 2.0 1 90.0 0.4002 211535.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1481 3.0 1 90.0 0.3741 211535.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1481 4.0 1 90.0 0.3497 211535.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1481 5.0 1 90.0 0.3268 211535.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1481 6.0 1 90.0 0.3055 211535.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1481 7.0 1 90.0 0.2856 211535.9\n",
      "we have 2 non-opposing shorted wedges for tract no 1485\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1486 0 372481.0079766508 0.4519\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1486 0 58420.90142556821 0.2259\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1486 0 470830.1647454089 0.5444\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1486 0 222610.95507806152 0.3851\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1486 0 63034.29663955112 0.3055\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1486 0 146120.77743732042 0.3453\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1486 0 186164.62585476157 0.3652\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1486 0 202134.39191194368 0.3752\n",
      "I am working on tract number 1500 of 2181 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1501\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1514 2.0 1 90.0 0.3703 257941.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1517 2.0 2 90.0 0.3987 213631.2\n",
      "I am working on tract number 1520 of 2181 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1539\n",
      "I am working on tract number 1540 of 2181 tracts\n",
      "I am working on tract number 1560 of 2181 tracts\n",
      "I am working on tract number 1580 of 2181 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1587 3.0 0 90.0 0.3339 196665.4\n",
      "I am working on tract number 1600 of 2181 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1610\n",
      "I am working on tract number 1620 of 2181 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1626\n",
      "I am working on tract number 1640 of 2181 tracts\n",
      "I am working on tract number 1660 of 2181 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1664\n",
      "I am working on tract number 1680 of 2181 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1681\n",
      "I am working on tract number 1700 of 2181 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1705\n",
      "I am working on tract number 1720 of 2181 tracts\n",
      "I am working on tract number 1740 of 2181 tracts\n",
      "I am working on tract number 1760 of 2181 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1763\n",
      "we have 2 non-opposing shorted wedges for tract no 1771\n",
      "I am working on tract number 1780 of 2181 tracts\n",
      "I am working on tract number 1800 of 2181 tracts\n",
      "I am working on tract number 1820 of 2181 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1834\n",
      "I am working on tract number 1840 of 2181 tracts\n",
      "I am working on tract number 1860 of 2181 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1875\n",
      "I am working on tract number 1880 of 2181 tracts\n",
      "I am working on tract number 1900 of 2181 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1916\n",
      "we have 2 non-opposing shorted wedges for tract no 1919\n",
      "I am working on tract number 1920 of 2181 tracts\n",
      "I am working on tract number 1940 of 2181 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1947\n",
      "I am working on tract number 1960 of 2181 tracts\n",
      "I am working on tract number 1980 of 2181 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1986\n",
      "we have 2 non-opposing shorted wedges for tract no 1987\n",
      "we have 2 non-opposing shorted wedges for tract no 1988\n",
      "we have 2 non-opposing shorted wedges for tract no 1989\n",
      "I am working on tract number 2000 of 2181 tracts\n",
      "I am working on tract number 2020 of 2181 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2035\n",
      "I am working on tract number 2040 of 2181 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2051\n",
      "we have 2 non-opposing shorted wedges for tract no 2056\n",
      "we have 2 non-opposing shorted wedges for tract no 2057\n",
      "I am working on tract number 2060 of 2181 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2060\n",
      "we have 2 non-opposing shorted wedges for tract no 2061\n",
      "we have 2 non-opposing shorted wedges for tract no 2065\n",
      "we have 2 non-opposing shorted wedges for tract no 2067\n",
      "we have 2 non-opposing shorted wedges for tract no 2069\n",
      "we have 2 non-opposing shorted wedges for tract no 2071\n",
      "we have 2 non-opposing shorted wedges for tract no 2072\n",
      "I am working on tract number 2080 of 2181 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2085\n",
      "we have 2 non-opposing shorted wedges for tract no 2086\n",
      "I am working on tract number 2100 of 2181 tracts\n",
      "I am working on tract number 2120 of 2181 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2123 5.0 0 90.0 0.4962 225881.9\n",
      "we have 2 non-opposing shorted wedges for tract no 2127\n",
      "I am working on tract number 2140 of 2181 tracts\n",
      "I am working on tract number 2160 of 2181 tracts\n",
      "I am working on tract number 2180 of 2181 tracts\n",
      "Sum of weights over all precinct home districts should have been 1.000, but was  1.0000438469115345\n",
      "Average and max number of wedgePop loops per tract were:  6.406694176983035 24.0\n",
      "min,average,max of Home District areas were:  0.0 0.16407469209087278 0.6724899079081893\n",
      "calculated statewide vote was 0.40618756206525486, should have been 0.4192871641320516\n",
      "calcd statewide Hispanic pop was 0.1905657014784311, should have been 0.19721588560300277\n",
      "calcd statewide Black pop was 0.11994075174813615, should have been 0.12856381612649373\n",
      "fraction of HDs that with altered wedge angles near boundaries =  0.4419990829894544\n"
     ]
    }
   ],
   "source": [
    "#3/7 - 3/10/22 - linear wideAngle, short the opposing wedgePop to match first shorted wedge (see nonper toy)\n",
    "# Bisect for yoyo > 3, precinct calcs after wedges finalized.  More aggressive Euler gain\n",
    "#minTractPop = 10  #this is set in a prior block\n",
    "pi=3.1415926536\n",
    "nWedges = 4  #number of wedges per home district polygon  \n",
    "avgWedgeAngle = 2*pi / nWedges\n",
    "wedgeTriAR = [math.sin(pi/nWedges)*math.cos(pi/nWedges)]*nWedges  #wedge triangle area ratio (= area / r*r)\n",
    "angle = [0.]*nWedges\n",
    "angle2 = [0.]*nWedges\n",
    "pt1 = [Point(0,0)]*nWedges  #these four define the polygon wedge for a growing home district\n",
    "pt2 = [Point(0,0)]*nWedges\n",
    "pt3 = [Point(0,0)]*nWedges\n",
    "pt4 = [Point(0,0)]*nWedges\n",
    "oldR = [0.]*nWedges  #wedge radius from previous loop\n",
    "# guessedR = [0.]*nWedges  #obsolete; was wedge radius if we extrapolate from population density of most recent wedge piece\n",
    "printPoly = [Polygon([(0,0),(0,1),(1,1)])]*nWedges #for debugging\n",
    "wedgePop = [0.]*nWedges\n",
    "tractLoopCounter = [0.]* nTracts  #this tracks how many loops per tract to get to target wedge Pop\n",
    "nearEdge = [0.]* nTracts   #not yet implemented; this will flag tracts near map edge for reprocessing\n",
    "tractUse = [0.] * nTracts  #this will store how much we use this tract in ALL HD's vs. expectation\n",
    "precinctUse = [0.] * nPrecincts   #same, but for each precinct / VTD\n",
    "loopTractUse = [0.] * nTracts  #same as above two, but only for the current loop / tract\n",
    "loopPrecinctUse = [0.] * nPrecincts\n",
    "HDvPop = [0.]*nTracts\n",
    "HDvGOP = [0.]*nTracts  #GOP lean of each tract's \"home district\"\n",
    "HDvBlack = [0.]*nTracts\n",
    "HDvHisp = [0.]*nTracts #pct Hispanic by home district\n",
    "HDweight = [0.]*nTracts  #relative weight of each district.  In absence of splits, will equal precinct pop\n",
    "HDarea = [0.]*nTracts  #geographical area of each home district\n",
    "HDradius = [[0.]*nWedges for t in range(nTracts)] #final wedge length for each Home District wedge\n",
    "HDangle = [[0.]*nWedges for t in range(nTracts)] #final included angle for each HD wedge\n",
    "angle0 = [-999.] * nTracts  #orientation of 0th wedge.  Random except re-oriented if we are near-boundary\n",
    "toler = 0.005  #adjustable - fractional slop in district population.  normally 0.01, to be reduced to 0.005\n",
    "tolerPop = toler * avgDistrictPop  #absolute slop in district pop\n",
    "nLoopPrint = 30  #at this loop number, we alert user of problem\n",
    "nGiveUp = 40  #we punt after this many loops\n",
    "nLoopSuperPrint = nGiveUp - 2 # at this loop number, we output wedge growth visuals\n",
    "wrongPop = [0.]*nTracts\n",
    "normalGain = 0.8  #adjustable - a bit less than 1.0 for stability, how far we step relative to expected perfect guess\n",
    "EulerGain = 1.5 #if we hit empty land, gain more aggressively to get through it\n",
    "#yoyoFactor = 0.3 #rate of reduction in gain as solver continues to yoyo in solving a wedge  #not currently used ***\n",
    "globalMax_dr = (MAP.area/nDistricts) ** 0.5  #put a reasonable upper limit on wedge radial growth step size\n",
    "tractPrintInterval = 20  #for tracking progress\n",
    "minTractArea = 0.00001 * MAP.area / nTracts  #failsafe for later div-by-zero\n",
    "homePopDensity = avgGridDensity  #seed this\n",
    "didWeRestart = [0] * nTracts  #will flag if we adjusted wedge angles due to a single shorted wedge\n",
    "maxAngleRatio = 1.9  #for tightening tract weighting near boundaries  #NEW 3/7/22\n",
    "levelL = 0.36 #sqrt(pop) for angle=std  These two control distortion in wedge angles relative to wedgePop shortness\n",
    "minWedgePop = [avgDistrictPop/nWedges] * nTracts  #wedge pop for wedge facing boundary (for tracts near boundaries)\n",
    "maxWideAngle = 0.8 * pi  #adjustable parameter.  (avoid wedges too close to half a pie\n",
    "\n",
    "for t in range(nTracts) :  #(nTracts):  #loop on each tract.  Start by resetting stats\n",
    "    gain = normalGain  #in case last precinct had convergence problems\n",
    "    if (t % tractPrintInterval) == 0 : \n",
    "        print(\"I am working on tract number {0} of {1} tracts\".format(t,nTracts) )\n",
    "    tractLoopCounter[t] = 0.  #for stability stats, will track how many times we need to loop for each tract\n",
    "    # isActiveG = [0]*nGrids  #resets whether each grid is relevant to this tract (not used; see alt grid turn-on method)\n",
    "    if (tractArea[t] > minTractArea) :  #too lazy to indent everything.  We'll catch zero tractArea later\n",
    "        homePopDensity = tractPop[t]/tractArea[t]  #temporary - estimate the local density for initial step\n",
    "    tractCP = Point(tractGeom[t].centroid.x,tractGeom[t].centroid.y)  #shorthand for centroid of each tract\n",
    "    for nG in range(nGrids):\n",
    "        if gridGeom[nG].contains(tractCP) :  #which grid contains the centroid of this tract?  Turn it on!\n",
    "            # isActiveG[nG] = 1  #I don't think I use this\n",
    "            homeGridDensity = gridDensity[nG]\n",
    "    maxStartDensity = max(homePopDensity,homeGridDensity)\n",
    "    minR = (avgDistrictPop / maxStartDensity / pi)**0.5   # conservative estimated radius of this tract's district\n",
    "    tinyR = 0.01 * minR   #ensures we start with small, but not infinitesimal wedge populations\n",
    "    angle0[t] = random.uniform(0,2.*pi)  #imparts random orientation to our starting wedge\n",
    "    loopTractUse = [0.]*nTracts  #this and below help us reset tract n precinct use after a wedge reset\n",
    "    loopPrecinctUse = [0.]*nPrecincts\n",
    "    # ***GROW WEDGES SIMULTANEOUSLY via ARRAYS, SO WE CAN REACT ON FLY TO BOUNDARY STOPS\n",
    "    HDpop = 0.  #running total of this tract's \"home district\" population\n",
    "    nActiveWedges = nWedges #active wedges have not run over boudnary\n",
    "    targetWedgePop = [avgDistrictPop / nWedges]*nWedges  #at beginning, split districtPop equally among wedges\n",
    "    #latestWedgeDensity = [0.]*nWedges  #stop using # pop'n density of the wedge piece we just added or subtracted\n",
    "    wedgePop = [0.]*nWedges\n",
    "    oldWedgePop = [0.]*nWedges  #wedge pop from previous round (needed for NR projection)\n",
    "    wedgePopGap = [0.]*nWedges\n",
    "    isOverEdge = [0] *nWedges #each wedge is still fully inside map boundary\n",
    "    isSatisfied = [0] * nWedges #wedges close enough to target are not improved upon\n",
    "    yoyoCount = [0] * nWedges #tracks how many times we've reversed this wedge's growth vs. shrink at current wPop target\n",
    "    wedgeMaxR = [0.] * nWedges #will track the largest radius this wedge has seen (to cap yoyo bisection step)\n",
    "    wedgeAngle = [avgWedgeAngle] * nWedges #reset to equi-angle when starting each tract    \n",
    "    currentR = [0.]*nWedges\n",
    "    old_dr = [0.]*nWedges\n",
    "    wedgeStop = 0  #obsolete? this will flag if we need to rejigger oldR when we stopped an over-boundary wedge\n",
    "    for nW in range(nWedges):\n",
    "        currentR[nW] = tinyR  #each wedge starts as a tiny triangle of this radius\n",
    "        old_dr[nW] = currentR[nW] #this will track the last radial step (to help convergence)\n",
    "    oldR = [0.]*nWedges   #for remembering last loop's wedge radius\n",
    "    max_dr = [globalMax_dr]*nWedges  #reset max possible positive radial step size\n",
    "    for nW in range(nWedges) :  #this loop: set up tiny wedges in each direction to seed each wedge loop\n",
    "        angle[nW] = (nW-0.5)*wedgeAngle[nW]+angle0[t]   #local variable for orientation of START of wedge\n",
    "        angle2[nW] = angle[nW]+wedgeAngle[nW]      #local angle for clockwise END of wedge\n",
    "        pt1[nW] = tractCP\n",
    "        pt2[nW] = Point(tractCP.x+currentR[nW]*math.cos(angle[nW]),  tractCP.y+currentR[nW]*math.sin(angle[nW]) )\n",
    "        pt3[nW] = Point(tractCP.x+currentR[nW]*math.cos(angle2[nW]), tractCP.y+currentR[nW]*math.sin(angle2[nW]) )\n",
    "        wedgePoly = Polygon([pt1[nW], pt2[nW], pt3[nW] ]) #build a tiny starter wedge triangle\n",
    "        wedgePop[nW] = tractPop[t]* wedgePoly.area/max(tractArea[t],minTractArea)  #max prevents rare div-by-zero\n",
    "    HDpop = np.sum(wedgePop)\n",
    "    if (tractPop[t] < minTractPop or tractArea[t] == 0 or isSkippedTract[t] == 1):\n",
    "        #go directly to jail, do not pass Go, this tract doesn't count\n",
    "        HDpop = avgDistrictPop  #white lie to kick us out of loop\n",
    "    while abs (HDpop - avgDistrictPop) > tolerPop :  #until we've grown the home district to the right size...\n",
    "        sumWedgePopGapChange = 0  #this will total wedge pops that fall short of expectations (when over boundary)\n",
    "        for nW in range(nWedges) :  #for each wedge, we'll build to gain pop or shrink to lose population ...\n",
    "            neededWedgePop = targetWedgePop[nW] - wedgePop[nW]\n",
    "            isSatisfied[nW] = 0  #default in case target changed last loop.  We'll check immediately below\n",
    "            if abs(neededWedgePop/targetWedgePop[nW]) < 0.5*toler :  #is this wedge close enough to stop iterating on it?\n",
    "                isSatisfied[nW] = 1\n",
    "            if isOverEdge[nW] == 0 and isSatisfied[nW] == 0:   #we skip over-boundary and near-perfect wedges\n",
    "                wedgePopDelta = wedgePop[nW] - oldWedgePop[nW]  #how much wedgePop gained in last loop\n",
    "                if (wedgePopDelta == 0): #our last wedge change was in an empty area (desert or off map)\n",
    "                    gainAdjust = EulerGain/gain  #Euler method often over-cautious at map edges or in sparse areas\n",
    "                    guessedRsquared = currentR[nW]*currentR[nW] * targetWedgePop[nW] / wedgePop[nW]  #Euler guess\n",
    "                    # can't use Newton-Raphson since last dy was zero, use Euler instead\n",
    "                    print(\"we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop\",t,tractLoopCounter[t],\n",
    "                          nW, round(180./pi*wedgeAngle[nW],1),round(currentR[nW],4),round(wedgePop[nW],1))\n",
    "                else: #use N-R estimation\n",
    "                    gainAdjust = 1.0\n",
    "                    R2delta = currentR[nW]*currentR[nW] - oldR[nW]*oldR[nW]  #this and below are run/rise of current slope\n",
    "                    guessedRsquared = currentR[nW]*currentR[nW] + R2delta / wedgePopDelta * neededWedgePop\n",
    "                    \n",
    "                guessedRsquared = max( guessedRsquared, 0. ) #minimum guardrail\n",
    "                guessed_dr = guessedRsquared ** 0.5 - currentR[nW]  #best guess for wedge radius change\n",
    "                #gain = max(0.3,normalGain ** (1. + yoyoFactor * yoyoCount[nW]))  #obsolete; using bisect\n",
    "                dr = gain * gainAdjust * guessed_dr   #gain typically ~0.8 for stability\n",
    "                dr = max( -0.5*currentR[nW],min(dr,max_dr[nW]) )  #apply min and max guardrails to wedge radius change\n",
    "                \n",
    "                if np.sign(dr) != np.sign(old_dr[nW]) :  #are we yoyoing? How many times?\n",
    "                    yoyoCount[nW] += 1\n",
    "                if yoyoCount[nW] > 3 and yoyoCount[nW] <= 6 :\n",
    "                    wedgeMaxR[nW] = max(wedgeMaxR[nW],currentR[nW]) #will track largest R in yoyo cycle\n",
    "                    max_dr[nW] = 2.*wedgeMaxR[nW]  #this will now store a reasonable max bisection step size\n",
    "                if yoyoCount[nW] > 6 :  #switch to bisection method; too many yo-yo's\n",
    "                    print(yoyoCount[nW],\"yoyos for tract,wedge,wedgePop,r=\",t,nW,wedgePop[nW],round(currentR[nW],4) )\n",
    "                    max_dr[nW] = 0.5 * max_dr[nW] #we are now bisecting ...\n",
    "                    if(neededWedgePop < 0):\n",
    "                        dr = max(-1.*max_dr[nW], -0.5*currentR[nW])  #reduce wedge size\n",
    "                    else:\n",
    "                        dr = max_dr[nW]  #increase wedge size\n",
    "                old_dr[nW] = dr  #these three lines -- save current as old values for next loop\n",
    "                oldR[nW] = currentR[nW]\n",
    "                oldWedgePop[nW] = wedgePop[nW]\n",
    "                if dr > 0. :  #we are growing a wedge trapezoid piece \n",
    "                    outerR = currentR[nW] + dr\n",
    "                    innerR = currentR[nW]\n",
    "                    currentR[nW] = outerR    #for next loop around\n",
    "                else:    #this wedge trapezoid piece will be SUBTRACTED from current wedge\n",
    "                    outerR = currentR[nW]\n",
    "                    innerR = currentR[nW] + dr  #remember, dr is negative here\n",
    "                    currentR[nW] = innerR             #for next loop around\n",
    "                #now, describe the new wedge to probe for precinct intersections ...\n",
    "                pt1[nW] = Point(tractCP.x+innerR*math.cos(angle[nW]), tractCP.y+innerR*math.sin(angle[nW]) )\n",
    "                pt2[nW] = Point(tractCP.x+outerR*math.cos(angle[nW]), tractCP.y+outerR*math.sin(angle[nW]) )\n",
    "                pt3[nW] = Point(tractCP.x+outerR*math.cos(angle2[nW]),tractCP.y+outerR*math.sin(angle2[nW]) )\n",
    "                pt4[nW] = Point(tractCP.x+innerR*math.cos(angle2[nW]),tractCP.y+innerR*math.sin(angle2[nW]) )\n",
    "                wedgePoly = Polygon([pt1[nW], pt2[nW], pt3[nW],pt4[nW]])  #true for wedge add-on or to-be-trimmed\n",
    "                \n",
    "                printPoly[nW] = wedgePoly  #for debugging\n",
    "                latestWedgePop = 0.  #for the new piece, not the entire triangle\n",
    "                usedTract = [0]*nTracts  #rezero lists of tracts and precincts that could straddle multiple grids\n",
    "                usedPrecinct = [0]*nPrecincts\n",
    "                for nG in range(nGrids) :  # loop over ACTIVE grids to look for intersecting tracts\n",
    "                    gridIntersxn = gridGeom[nG].intersection(wedgePoly)\n",
    "                    if (gridIntersxn.area > 0) :  #this grid is RELEVANT to this wedge\n",
    "                        for tt in range(nGridTracts[nG]) : #look for intersxns with all tracts in this grid\n",
    "                            nTT = gridTractNo[nG][tt] #shorthand for this tract's global tract no\n",
    "                            if(usedTract[nTT] == 0) :  #Did we not already look at this tract in another grid list?\n",
    "                                usedTract[nTT] = 1  #well, now we have!  Probe intersection with wedge\n",
    "                                overlap = tractGeom[nTT].intersection(wedgePoly).area\n",
    "                                if overlap > 0 :\n",
    "                                    fracArea = overlap/tractArea[nTT]\n",
    "                                    latestWedgePop  += fracArea*tractPop[nTT]  #always positive (used in density calc)\n",
    "                                    loopTractUse[nTT] += np.sign(dr)*overlap/tractArea[nTT] * tractPop[t]/avgDistrictPop\n",
    "                        # found all possible tract overlaps with this increment / decrement to this wedge.\n",
    "                        \n",
    "                wedgePop[nW] += np.sign(dr)*latestWedgePop  #for full triangle, based on this latest piece\n",
    "                # Now, flag if we're beyond boundary\n",
    "                if wedgePop[nW] < targetWedgePop[nW] : #if growing, confirm we're not beyond MAP boundary\n",
    "                    leadingEdge = LineString([pt2[nW],pt3[nW]])\n",
    "                    if leadingEdge.intersects(MAP) :  #still room to grow in part of edge, keep going\n",
    "                        gerrymandering = \"evil\"  #it had to be said\n",
    "                    else:  #this wedge is fully beyond the map. give up on more map intersection\n",
    "                        isOverEdge[nW] = 1\n",
    "                        shortedWedge = nW  #ID'ing highest numbered wedge that got shorted by the boundary in this loop\n",
    "                        oldWedgePopGap = wedgePopGap[nW]\n",
    "                        wedgePopGap[nW] = targetWedgePop[nW] - wedgePop[nW]  #how far this wedge's pop is below its target\n",
    "                        nActiveWedges -= 1  #a few lines below, we will adjust other wedge's targets\n",
    "                        sumWedgePopGapChange += wedgePopGap[nW] - oldWedgePopGap\n",
    "                        if (nActiveWedges == 0) : #we're doomed, somehow all wedges are short and off map\n",
    "                            print(\"PUNT! no more active wedges for tract, loop =\",t,tractLoopCounter[t] )\n",
    "                            tractLoopCounter[t] = nGiveUp + 1  #PUNT !!!\n",
    "                            \n",
    "                        max_dr = [globalMax_dr]*nWedges   #allow other wedges to take big steps again to catch up\n",
    "                        yoyoCount = [0] * nWedges  #go back to original gains on ALL active wedges\n",
    "                   \n",
    "        # end of nW loop to adjust all wedge populations by growing or trimming wedges, IDing over-edge wedges\n",
    "        tractLoopCounter[t] +=1   # still looping on home district Pop.\n",
    "        nReceivingWedges = nActiveWedges\n",
    "        oppFlag = 0\n",
    "        if targetWedgePop[int(nWedges/2)] < 0.99 * avgDistrictPop/float(nWedges): #is opposite wedge restricted?\n",
    "            oppFlag = 1\n",
    "            nReceivingWedges -= 1-isOverEdge[int(nWedges/2)]  #if yes, don't count opposite wedge as a receiver\n",
    "        if nReceivingWedges == 0: #rarely, the opposite wedge is the only active one\n",
    "            for nWW in range(nWedges): #in this case, we allow the opposite wedge to pick up the slack\n",
    "                targetWedgePop[nWW] += sumWedgePopGapChange/max(1.,float(nReceivingWedges) )\n",
    "        else: #if the opp wedge has a ceiling wedgePopTarget, distribute wedgePopGap to other active wedges\n",
    "            for nWW in range(nWedges):  #time to make adjustments in target wedge pops based on boundary fails\n",
    "                if nWW != int(nWedges/2) or oppFlag == 0 :  #excluding opposite wedge as a receiver\n",
    "                    targetWedgePop[nWW] += sumWedgePopGapChange/float(nReceivingWedges)\n",
    "\n",
    "        # *** NEW 1/19/22 CODE TO ADJUST WEDGE ANGLES WHEN A BOUNDARY ENCOUNTERED\n",
    "        if (nActiveWedges == nWedges or nActiveWedges < nWedges - 2\n",
    "           or didWeRestart[t] == 1) :  #0 or 3+ over-boundary short wedges, or we've already adjusted wedge angles once\n",
    "            HDpop = np.sum(wedgePop) #Keep going with normal wedge growth and trim process\n",
    "        else :  # 1 OR 2 SHORTED WEDGES.  MAY WANT TO ALTER WEDGE ANGLES\n",
    "            didWeRestart[t] = 1  #to ensure we adjust wedge angles and target wedgePops at most once per tract\n",
    "            if (nActiveWedges == nWedges - 2) :  #exactly two wedges got shorted in this loop -- are they opposing?\n",
    "                oppW = int( (shortedWedge + nWedges/2) % nWedges)  #the index of wedge opposite the last shorted wedge\n",
    "                if (isOverEdge[oppW] == 0) :\n",
    "                    print(\"we have 2 non-opposing shorted wedges for tract no\",t)\n",
    "                    totalLiveAngle = 2.*pi - np.sum(isOverEdge)*avgWedgeAngle  #avail angle to divvy among live wedges  \n",
    "                    for nW in range (nWedges) : #Decrease wedge angles opposite shorted wedges via a complex weighting\n",
    "                        if isOverEdge[nW] == 0 :\n",
    "                            oppW = int( (nW + nWedges/2) % nWedges) \n",
    "                            angleWeight = (np.sum(wedgePopGap)-wedgePopGap[oppW])/np.sum(wedgePopGap)/(nActiveWedges-1)\n",
    "                            wedgeAngle[nW] = angleWeight * totalLiveAngle  \n",
    "                else : #shorted wedges ARE opposing\n",
    "                    print(\"we have 2 OPPOSING shorted wedges for tract no\",t) # no wedge-angle adjustment in this case\n",
    "                    #wedgeAngle=[avgWedgeAngle]*nWedges  #comment this out, no change\n",
    "            else: # a single shorted wedge (over boundary).  COMPLETELY RESTART wedge growth process\n",
    "                shortW = shortedWedge #Next dozen lines: convoluted code to find angle to closest boundary point\n",
    "                #print(\"1 wedge over map boundary for tract\",t,\"at loop, wedgePop\",tractLoopCounter[t],wedgePop[shortW] )\n",
    "                shortedPoly = Polygon([tractCP, pt2[shortW],pt3[shortW] ])\n",
    "                MAPedge = shortedPoly.intersection(MAP.exterior) #true state boundary line where wedge crossed it\n",
    "                minDistance = max(tractCP.distance(MAPedge),tinyR)  #closest distance to edge where wedge hit the boundary\n",
    "                edgeCircle = tractCP.buffer(1.01*minDistance)  #build a circle a little bigger than this distance\n",
    "                #  1.001 is not big enough; will occasionally not intersect\n",
    "                closeArea = edgeCircle.intersection(MAPedge)\n",
    "                counter = 0.\n",
    "                maxCounter = 10. #give up after 10 tries\n",
    "                while closeArea.is_empty :  # protect against missing the map boundary somehow\n",
    "                    print(\"need to widen edge circle beyond\",minDistance, \"for tract (x,y)=(\",tractCP.x,tractCP.y)\n",
    "                    minDistance = minDistance * 1.1\n",
    "                    edgeCircle = tractCP.buffer(minDistance)  #widen the circle\n",
    "                    closeArea = edgeCircle.intersection(MAPedge)\n",
    "                    counter += 1\n",
    "                    if counter >= maxCounter: #not finding map edge intersection.  Try another way\n",
    "                        print(\"did not find boundary with circle approach.  Brute-force it.\")\n",
    "                        minDistance = tractCP.distance(MAP.exterior)\n",
    "                        edgeCircle = tractCP.buffer(1.01*minDistance)\n",
    "                        closeArea = edgeCircle.intersection(MAP.exterior)                        \n",
    "            \n",
    "                closePoint = closeArea.centroid  # this is a point very close to the closest beeline from tract to map edge\n",
    "                #print(closePoint,tractCP, \"are boundary point and tract center point\")\n",
    "                x1 = closePoint.x\n",
    "                x2 = tractCP.x  #debugging\n",
    "                dx = closePoint.x - tractCP.x\n",
    "                dy = closePoint.y - tractCP.y\n",
    "                exitAngle = pi/2. * np.sign(dy)  #default in case dx=0\n",
    "                if (dx != 0 ):\n",
    "                    exitAngle = math.atan(dy/dx)  #this is the angle from the x-axis to the exit beeline\n",
    "                    if dx < 0 :  #use complementary atan solution; boundary is west of tract centroid\n",
    "                        exitAngle = pi + exitAngle\n",
    "                angle0[t] = exitAngle # this reorients 0th wedge to face boundary's closest point               \n",
    "                # New 1/23/22 - let's estimate wedgePopGap at ideal orientation, normal angle   # ****************\n",
    "                wedgeAngle1 = exitAngle - 0.5*avgWedgeAngle\n",
    "                wedgeAngle2 = exitAngle + 0.5*avgWedgeAngle\n",
    "                maxR = max(stateWidth,stateHeight) #ensuring we make a big enough wedge\n",
    "                wedgePt1 = tractCP\n",
    "                wedgePt2 = Point(tractCP.x+maxR*math.cos(wedgeAngle1),  tractCP.y+maxR*math.sin(wedgeAngle1) )\n",
    "                wedgePt3 = Point(tractCP.x+maxR*math.cos(wedgeAngle2), tractCP.y+maxR*math.sin(wedgeAngle2) )\n",
    "                wedgePoly = Polygon([ wedgePt1, wedgePt2, wedgePt3 ])\n",
    "                minWedgePop[t] = 0. #the wedgePop of the shorted wedge if we re-orient but don't adjust angles\n",
    "                usedTract = [0]*nTracts  #rezero lists of tracts and precincts that could straddle multiple grids\n",
    "                for nG in range(nGrids) :  # loop over ACTIVE grids to look for intersecting tracts\n",
    "                    gridIntersxn = gridGeom[nG].intersection(wedgePoly)\n",
    "                    if (gridIntersxn.area > 0) :  #this grid is RELEVANT to this wedge\n",
    "                        for tt in range(nGridTracts[nG]) : #look for intersxns with all tracts in this grid\n",
    "                            nTT = gridTractNo[nG][tt] #shorthand for this tract's global tract no\n",
    "                            if(usedTract[nTT] == 0) :  #Did we not already look at this tract in another grid list?\n",
    "                                usedTract[nTT] = 1  #well, now we have!  Probe intersection with wedge\n",
    "                                overlap = tractGeom[nTT].intersection(wedgePoly).area\n",
    "                                if overlap > 0 :\n",
    "                                    fracArea = overlap/tractArea[nTT]\n",
    "                                    minWedgePop[t]  += fracArea*tractPop[nTT]  #always positive (used in density calc)\n",
    "                #print(\"the minimum shorted Wedge Pop for tract {0} is {1}\".format(t,minWedgePop[t]) )  #debug\n",
    "                #printAngle[t] = round(exitAngle*180./pi,4) #for debugging\n",
    "                printDist = round(minDistance,4)\n",
    "                #print(t,\"(\",tractCP.x,tractCP.y,\")\",printAngle,printDist,\"t,tract(x,y),exitAngle,dist\")\n",
    "                # ********************************************************************\n",
    "                # printWedgePopGap[t] = wedgePopGap[shortW]\n",
    "                #now, we \"squish the square\" based on how close we are to boundary - see offline documentation\n",
    "                equivL = (4.*minWedgePop[t] / avgDistrictPop)**0.5  #non-dimensional centroid-boundary distance\n",
    "                wideAngle = avgWedgeAngle*max(  1,( 1.+(maxAngleRatio-1.)*(levelL - equivL)/(levelL - 0.) )  )\n",
    "                wideAngle = min(maxWideAngle, wideAngle)  #prevent too wide of an angle (half-pie)\n",
    "                #HERE, WE ADJUST all wedge ANGLES\n",
    "                wideAngle = min(wideAngle, maxWideAngle*pi) #set an upper limit to avoid gaping wedges\n",
    "                thinAngle = (2.*pi - 2.*wideAngle)/(nWedges - 2.)  #other wedges equally angle-compressed\n",
    "                for nW in range(nWedges) :\n",
    "                    if (nW == 0 or nW == int(nWedges/2) ) : #assign these two as shorted wedge and its opposite\n",
    "                        wedgeAngle[nW] = wideAngle\n",
    "                    else :\n",
    "                        wedgeAngle[nW] = thinAngle\n",
    "                #** COMPLETELY RESTART WEDGE GROWTH PROCESS FROM INITIAL PIZZA SLICES, NOW RE-ORIENTED\n",
    "                loopTractUse = [0.] * nTracts  # reset as we are starting over\n",
    "                loopPrecinctUse = [0.]* nPrecincts\n",
    "                HDpop = 0.\n",
    "                nActiveWedges = nWedges\n",
    "                # NEW 3/10/22 - we not only squish the angles, we short the target for the wedge opposite boundary\n",
    "                oppWedgeExpectedPop = minWedgePop[t] * math.tan(0.5*wideAngle)/math.tan(pi/nWedges)  #prediction for new wedge0's pop\n",
    "                oppWedgeExpectedPop = min(oppWedgeExpectedPop,avgDistrictPop/nWedges) #not needed, but just in case...\n",
    "                for nW in range(nWedges):\n",
    "                    if nW == int(nWedges/2):\n",
    "                        targetWedgePop[nW] = oppWedgeExpectedPop\n",
    "                    else:  #NOTE: we don't mess w wedge0's target.  When it shorts again, we will adjust other wedge's tgts\n",
    "                        targetWedgePop[nW] =(avgDistrictPop - oppWedgeExpectedPop)/float(nWedges-1)\n",
    "                # *** END OF 3/10/22 MODIFICATION\n",
    "                oldR = [0.]*nWedges   #for remembering last loop's wedge radius  \n",
    "                currentR = [tinyR]*nWedges                \n",
    "                old_dr = [tinyR]*nWedges  #this will track the last radial step (to help convergence)\n",
    "                max_dr = [globalMax_dr]*nWedges  #reset max step size\n",
    "                wedgePop = [0.]*nWedges\n",
    "                oldWedgePop = [0.]*nWedges #wedge pop from previous round\n",
    "                wedgePopGap = [0.]*nWedges\n",
    "                isOverEdge = [0] *nWedges\n",
    "                isSatisfied = [0] *nWedges\n",
    "                yoyoCount = [0] *nWedges #just in case we missed this earlier\n",
    "                wedgeMaxR = [0.] * nWedges #resetting\n",
    "                angle[0] = angle0[t] - 0.5*wideAngle  #the 0th wedge always faces the boundary\n",
    "                angle2[0] = angle[0] + wedgeAngle[0]\n",
    "                for nW in range(nWedges) :\n",
    "                    if (nW == 0):\n",
    "                        angle[nW] = angle0[t] - 0.5*wideAngle  #we have to start somewhere :-)\n",
    "                    else :\n",
    "                        angle[nW] = angle2[nW-1]\n",
    "                    angle2[nW] = angle[nW]+wedgeAngle[nW]      \n",
    "                    pt1[nW] = tractCP\n",
    "                    pt2[nW] = Point(tractCP.x+tinyR*math.cos(angle[nW]),  tractCP.y+tinyR*math.sin(angle[nW]) )\n",
    "                    pt3[nW] = Point(tractCP.x+tinyR*math.cos(angle2[nW]), tractCP.y+tinyR*math.sin(angle2[nW]) )\n",
    "                    wedgePoly = Polygon([pt1[nW], pt2[nW], pt3[nW] ])\n",
    "                    wedgePop[nW] = tractPop[t]* wedgePoly.area/max(tractArea[t],minTractArea)\n",
    "                HDpop = np.sum(wedgePop)   #** END OF RESTART BLOCK.  RE-ENTER MAIN LOOP FOR THIS TRACT\n",
    "                      \n",
    "        # **** BELOW IS TO CORRECT FOR NONCONVERGENCE *****\n",
    "        if (tractLoopCounter[t] > nLoopPrint):  #may be becoming unstable. Alert user,    #and (don't) reduce gain\n",
    "            strPop = str(round(HDpop,2))\n",
    "            strWdg = \"Overedge?\"\n",
    "            strSat = \"Satisfied?\"\n",
    "            strYoyo = \"yoyo?\"                \n",
    "            strTWP_dr = \"tWP,dr\"\n",
    "            for nWW in range(nWedges) :\n",
    "                strPop = strPop + \", \"+str(round(wedgePop[nWW],1) )\n",
    "                strWdg = strWdg +str(isOverEdge[nWW])+\", \"\n",
    "                strSat = strSat +str(isSatisfied[nWW])\n",
    "                strYoyo = strYoyo + str(yoyoCount[nWW])\n",
    "                strTWP_dr = strTWP_dr + \", \"+str(round(targetWedgePop[nWW],1) )+\",\"+str(round(old_dr[nWW],4) )\n",
    "            print(\"loop{0}, tr{1},wedgePops{2}, {3},{4},{5} \".format(tractLoopCounter[t],t,strPop,strWdg,strSat,strYoyo) )\n",
    "            print(\"   targetWP, latest drx4 are\",strTWP_dr)\n",
    "\n",
    "        if (tractLoopCounter[t] > nLoopSuperPrint) :\n",
    "            for nWW in range(nWedges):                \n",
    "                print(\"wedge no, currentR,oldWedgePop, wedgePop, overEdge?\",nWW,str(round(currentR[nWW],5)), \n",
    "                      str(round(oldWedgePop[nWW],4)), str(round(wedgePop[nWW],4)),isOverEdge[nWW])   #debug\n",
    "                x, y = printPoly[nWW].exterior.xy     #wedge debugging .....\n",
    "                plt.plot(x, y, c=(0.1, 0.2, 0.02+float(nWW)/nWedges) )\n",
    "            plt.show()\n",
    "        if(tractLoopCounter[t] >= nGiveUp):\n",
    "            print(\"I looped {0} times on tract {1}, giving up w pop {2}\".format(nGiveUp,t,HDpop) )\n",
    "            wrongPop[t] = HDpop  #this will flag this HD as triaged\n",
    "            HDpop = avgDistrictPop  #white lie to kick out of loop\n",
    "        # *** END OF TRIAGE FOR NONCONVERGENCE\n",
    "        \n",
    "    # END OF WHILE LOOP --> we are within tolerance of avgDistrictPop. Finalize this tract's Home District stats\n",
    "    for nTT in range (nTracts) :\n",
    "        tractUse[nTT] += loopTractUse[nTT]\n",
    "    totGOP = 0.\n",
    "    totVote = 0.\n",
    "    totVAP = 0.\n",
    "    totHisp = 0.\n",
    "    totBlack = 0.  #zero these out prior to summing over final wedges\n",
    "    usedTract = [0]*nTracts  #rezero lists of tracts and precincts that could straddle multiple grids\n",
    "    usedPrecinct = [0]*nPrecincts\n",
    "\n",
    "    if (tractPop[t] < minTractPop or tractArea[t] == 0 or isSkippedTract[t] == 1): \n",
    "        #we bypassed the big loop; this tract is inconsequential\n",
    "        HDvPop[t] = HDpop  #a white lie\n",
    "        HDweight[t] = 0.000001  #to suppress this in total stats\n",
    "    else :\n",
    "        HDvPop[t] = np.sum(wedgePop)\n",
    "        # HDvHisp[t] = np.sum(wedgeHisp)/np.sum(wedgePop)  #3/2/22 - move to final wedge calcs\n",
    "        # HDvBlack[t] = np.sum(wedgeBlack)/np.sum(wedgePop)  #3/2/22 - move to final wedge calcs\n",
    "        HDweight[t] = tractPop[t]/np.sum(tractPop)\n",
    "        nearEdge[t] = nWedges - nActiveWedges  #flagging the number of wedges that were not completely pop-filled\n",
    "        centerPt = tractCP\n",
    "        outerPt2 = Point(tractCP.x+currentR[0]*math.cos(angle[0]), tractCP.y+currentR[0]*math.sin(angle[0]) )\n",
    "        outerPt3 = Point(tractCP.x+currentR[0]*math.cos(angle2[0]),tractCP.y+currentR[0]*math.sin(angle2[0]) )\n",
    "        HDpolly = Polygon([centerPt,outerPt2,outerPt3])   #initiate a polygon that will be the home district\n",
    "        for nW in range(nWedges):\n",
    "            HDradius[t][nW] = currentR[nW]\n",
    "            HDangle[t][nW] = angle[nW]\n",
    "            \n",
    "        for nW in range(1,nWedges):  #save final geometry, compute minority and partisan stats\n",
    "            cR = currentR[nW] #shorthand\n",
    "            outerPt2 = Point(tractCP.x+cR*math.cos(angle[nW]), tractCP.y+cR*math.sin(angle[nW]) )\n",
    "            outerPt3 = Point(tractCP.x+cR*math.cos(angle2[nW]),tractCP.y+cR*math.sin(angle2[nW]) )\n",
    "            HDpolly =  HDpolly.union( Polygon([centerPt,outerPt2,outerPt3]) )  #add this wedge to HD district polygon\n",
    "        HDpolly = HDpolly.intersection(MAP)  #exclude map's convex hull and buffer, just the original union of precincts\n",
    "        HDarea[t] = HDpolly.area  #for stats, final Home District area\n",
    "        for nG in range(nGrids) :  # ID the ACTIVE grids to look for intersecting precincts\n",
    "            gridIntersxn = gridGeom[nG].intersection(HDpolly)\n",
    "            if (gridIntersxn.area > 0) :  #this grid is RELEVANT to the final Home District\n",
    "                for tt in range(nGridTracts[nG]):\n",
    "                    nTT = gridTractNo[nG][tt]\n",
    "                    if usedTract[nTT] == 0: #only examine tracts that have not already been called for intersection\n",
    "                        usedTract[nTT] == 1  #this tract has now been called \n",
    "                        overlap = tractGeom[nTT].intersection(HDpolly).area\n",
    "                        if overlap > 0 :\n",
    "                            fracArea = overlap/tractArea[nTT]\n",
    "                            totVAP += fracArea*tractVAP[nTT]\n",
    "                            totHisp += fracArea*tractHisp[nTT]\n",
    "                            totBlack += fracArea*tractBlack[nTT]\n",
    "                        \n",
    "                for pp in range(nGridPrecincts[nG]): #scanning the list of precincts in this active grid\n",
    "                    nPP = gridPrecinctNo[nG][pp]\n",
    "                    if usedPrecinct[nPP] == 0  :\n",
    "                        usedPrecinct[nPP] = 1  #don't double up on precinct intersection\n",
    "                        overlap = vtdGeom[nPP].intersection(HDpolly).area\n",
    "                        if overlap > 0 :\n",
    "                            fracArea = overlap/vtdArea[nPP]\n",
    "                            totGOP += fracArea*vtdGOP[nPP]*vtdPop[nPP]\n",
    "                            totVote += fracArea*vtdPop[nPP]\n",
    "                            loopPrecinctUse[nPP] += overlap/vtdArea[nPP] *tractPop[t]/avgDistrictPop\n",
    "        HDvHisp[t] = totHisp/totVAP\n",
    "        HDvBlack[t] = totBlack/totVAP\n",
    "        HDvGOP[t] = totGOP / totVote\n",
    "        for nPP in range (nPrecincts) :\n",
    "            precinctUse[nPP] += loopPrecinctUse[nPP] #add to global use for this precinct\n",
    "            \n",
    "        \n",
    "\n",
    "    # end of loop on this tract\n",
    "for t in range(nTracts):\n",
    "    if(wrongPop[t] > 0):\n",
    "        HDvPop[t] = wrongPop[t]  #undo the lie that got us out of the loop early\n",
    "HDsumWeight = np.sum(HDweight)\n",
    "print(\"Sum of weights over all precinct home districts should have been 1.000, but was \",HDsumWeight)\n",
    "print(\"Average and max number of wedgePop loops per tract were: \",np.average(tractLoopCounter),np.max(tractLoopCounter) )\n",
    "print(\"min,average,max of Home District areas were: \",np.min(HDarea),np.average(HDarea),np.max(HDarea) )\n",
    "stateGOP2 = 0.\n",
    "stateHisp2 = 0.\n",
    "stateBlack2 = 0.\n",
    "for t in range(nTracts):\n",
    "    HDweight[t] = HDweight[t]/HDsumWeight   #renormalizing\n",
    "    stateGOP2 += HDweight[t]*HDvGOP[t]\n",
    "    stateBlack2 += HDweight[t]*HDvBlack[t]\n",
    "    stateHisp2 += HDweight[t]*HDvHisp[t]\n",
    "statePop = np.sum(tractPop)\n",
    "print(\"calculated statewide vote was {0}, should have been {1}\".format(stateGOP2, stateGOP) )\n",
    "print(\"calcd statewide Hispanic pop was {0}, should have been {1}\".format(stateHisp2, np.sum(tractHisp)/stateVAP ) )\n",
    "print(\"calcd statewide Black pop was {0}, should have been {1}\".format(stateBlack2, np.sum(tractBlack)/stateVAP ) )\n",
    "print(\"fraction of HDs that with altered wedge angles near boundaries = \",np.sum(didWeRestart)/nTracts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "24a347a9-8220-4c1a-bcfc-14923f1f98f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sum of weights over all precinct home districs should have been 1.000, but was  1.0000438469115345\n",
      "Average and max number of wedgePop loops per tract were:  6.406694176983035 24.0\n",
      "min,average,max of Home District areas were:  0.0 0.16407469209087278 0.6724899079081893 for  NJ\n"
     ]
    }
   ],
   "source": [
    "#use this line to document progress\n",
    "# start NJ 9:42, end 10:43\n",
    "print(\"Sum of weights over all precinct home districs should have been 1.000, but was \",HDsumWeight)\n",
    "print(\"Average and max number of wedgePop loops per tract were: \",np.average(tractLoopCounter),np.max(tractLoopCounter) )\n",
    "print(\"min,average,max of Home District areas were: \",np.min(HDarea),np.average(HDarea),np.max(HDarea),\"for \",STATE )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "72247942-899a-4ab2-9796-239aa1e524fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NJ 25Mar\n"
     ]
    }
   ],
   "source": [
    "date = \"25Mar\"\n",
    "print(STATE, date)  #check that I will not overwrite existing file :-)\n",
    "tractCPx = [0.]*nTracts\n",
    "tractCPy = [0.]*nTracts\n",
    "tractNo = [0.]*nTracts\n",
    "for t in range(nTracts):\n",
    "    tractCPx[t]=tractGeom[t].centroid.x\n",
    "    tractCPy[t]=tractGeom[t].centroid.y\n",
    "    tractNo[t]=t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "c55ac5fd-7402-47b9-9093-10b9fe0912b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#LET'S WRITE AN OUTPUT FILE BEFORE WE FORGET :-)\n",
    "#convert HD wedge geometries to 1D arrays.  BELOW IS FOR 4-WEDGE.  CAN AUGMENT FOR 6-WEDGE\n",
    "HDangle0 = [0.]*nTracts\n",
    "HDradius0 = [0.]*nTracts\n",
    "HDangle1 = [0.]*nTracts\n",
    "HDradius1 = [0.]*nTracts\n",
    "HDangle2 = [0.]*nTracts\n",
    "HDradius2 = [0.]*nTracts\n",
    "HDangle3 = [0.]*nTracts\n",
    "HDradius3 = [0.]*nTracts\n",
    "for t in range(nTracts):\n",
    "    HDangle0[t] = HDangle[t][0]\n",
    "    HDradius0[t] = HDradius[t][0]\n",
    "    HDangle1[t] = HDangle[t][1]\n",
    "    HDradius1[t] = HDradius[t][1]\n",
    "    HDangle2[t] = HDangle[t][2]\n",
    "    HDradius2[t] = HDradius[t][2]\n",
    "    HDangle3[t] = HDangle[t][3]\n",
    "    HDradius3[t] = HDradius[t][3]\n",
    "# now convert output to pandas dataframe and export\n",
    "\n",
    "paramList = [\"STATE\",\"stateGOP\",\"nDistricts\",\"nTracts\",\"nPrecincts\",\"nWedges\",\"popn-toler\",\n",
    "             \"gain\",\"maxWideAngle\",\"levelL\"]\n",
    "paramValues = [STATE,stateGOP, nDistricts, nTracts,nPrecincts,nWedges, toler, gain, maxWideAngle, levelL]\n",
    "for i in range(nTracts-len(paramList)):\n",
    "    paramList.append(\".\")\n",
    "    paramValues.append(-99)  #so all columns have same number of entries, even the parameter list\n",
    "df = pd.DataFrame( {\"paramList\": paramList,\"paramValues\":paramValues,\"HD-pop\":HDvPop,\"HDvGOP\":HDvGOP,\"HDvHisp\":HDvHisp,\n",
    "                    \"HDvBlack\":HDvBlack,\"HDwt\":HDweight,\"HDarea\":HDarea, \"HDangle0\":HDangle0, \"HDangle1\":HDangle1,\n",
    "                    \"HDangle2\":HDangle2,\"HDangle3\":HDangle3,\"HDradius0\":HDradius0,\"HDradius1\":HDradius1,\n",
    "                    \"HDradius2\":HDradius2,\"HDradius3\":HDradius3,\"startAngle\":angle0,\"tractNo\":tractNo,\n",
    "                    \"Loops\":tractLoopCounter,\"tractPop\":tractPop,\"tractHisp\":tractHisp,\"tractBlack\":tractBlack,\n",
    "                    \"centroid x\":tractCPx,\"centroid y\":tractCPy, \"tractUse\":tractUse,\"nearEdge\":nearEdge,\n",
    "                    \"wrongPop\":wrongPop,\"Restart?\":didWeRestart} ) \n",
    "\n",
    "outname = STATE+str(nDistricts)+\"HD1tol\"+str(toler)+\"nW\"+str(nWedges)+date+\".csv\"\n",
    "outpath = \"state_HD_output/\"+outname\n",
    "df.to_csv(outpath)\n",
    "precinctNo = [0.]*nPrecincts\n",
    "vtdX = [0.]*nPrecincts\n",
    "vtdY = [0.]*nPrecincts\n",
    "for p in range(nPrecincts):\n",
    "    precinctNo[p]=p\n",
    "    vtdX[p] = vtdGeom[p].centroid.x\n",
    "    vtdY[p] = vtdGeom[p].centroid.y\n",
    "df2 = pd.DataFrame( {\"precinctNo\":precinctNo,\"precinctPop\":vtdPop,\"precUse\":precinctUse, \"vtdX\":vtdX, \"vtdY\":vtdY} )\n",
    "outname2 = STATE+date+\"_VTD_tol\"+str(toler)+\"nW\"+str(nWedges)+\".csv\"\n",
    "outpath = \"state_HD_output/\"+outname2\n",
    "df2.to_csv(outpath)  #currently, I'm outputting the precinct use stats to a separate file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "c4449da0-47a9-40cc-a585-8288447290e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we will redo tract numbers  [0]\n"
     ]
    }
   ],
   "source": [
    "# Looking for nonconvergers ...  Run even if none observed, just to check  \n",
    "redo = [0]\n",
    "counter = 0.\n",
    "for t in range (nTracts) :\n",
    "    if wrongPop[t] > 0 :\n",
    "        ratio = round(wrongPop[t]/avgDistrictPop,2)\n",
    "        tractX = tractGeom[t].centroid.x\n",
    "        tractY = tractGeom[t].centroid.y        \n",
    "        print(t, ratio,\"(\",tractX,tractY,\")\",HDvGOP[t], \"t, pop/target,(x,y), pctR\")\n",
    "        if (ratio < 0.9 or ratio > 1.1) :  #these we will redo\n",
    "            plt.text(tractGeom[t].centroid.x, tractGeom[t].centroid.y,ratio, fontsize=14)\n",
    "            if counter == 0 :  #first redo\n",
    "                redo = [t]\n",
    "            else :\n",
    "                redo.append(t)\n",
    "            counter+= 1\n",
    "        else: #show these milder offenders on the map in a smaller font            \n",
    "            plt.text(tractGeom[t].centroid.x, tractGeom[t].centroid.y,ratio, fontsize=9)\n",
    "\n",
    "x,y = tractMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"purple\")\n",
    "plt.show()\n",
    "print(\"we will redo tract numbers \",redo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "be6e965b-4ca8-4af2-8031-63132fbb939d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1027 0.6736699019053821 0.012211792123723213 pop,GOP 0.50650721069293\n",
      "1030 0.6946739134022429 674.1466513599314 pop,GOP 0.50650721069293\n",
      "1150 0.698641932882413 0.7740009479286085 pop,GOP 0.4802846131040617\n",
      "1202 0.685857416218818 0.06676186994371297 pop,GOP 0.47477108903175524\n",
      "1209 0.688561917614297 0.33983242154255155 pop,GOP 0.4747710890317553\n",
      "3944 0.0 470.47348342007365 pop,GOP 0.5849803231803389\n",
      "4165 0.693879384747625 0.09220675674692412 pop,GOP 0.5973763874873864\n",
      "4277 0.0 470.47348262109534 pop,GOP 0.5849803231618343\n",
      "4372 0.6917853718740167 0.8368478117654664 pop,GOP 0.7263427109974424\n",
      "7246 0.6658316365726646 297.35375431128944 pop,GOP 0.5884575945183104\n",
      "7249 0.6430112770416312 297.27313248970864 pop,GOP 0.5884727553169233\n",
      "7261 0.6725027079139315 297.27910148450115 pop,GOP 0.5884726435215971\n",
      "7263 0.6792419936726547 297.29360306124875 pop,GOP 0.5884714850802175\n",
      "7272 0.6769013420236364 297.2711367589308 pop,GOP 0.5884734371468751\n",
      "7277 0.6695580816818475 297.27620860165536 pop,GOP 0.5884736110125854\n",
      "7280 0.6558571356057726 297.27119723795715 pop,GOP 0.588473439220166\n",
      "7398 0.6847527257076019 1670.8146530477215 pop,GOP 0.6283149331434802\n",
      "7401 0.6401892201943744 297.28519839548903 pop,GOP 0.588475906911854\n",
      "7426 0.6506169634663846 297.27430334955426 pop,GOP 0.5884695381460732\n",
      "7427 0.6522806901756246 297.27120241692694 pop,GOP 0.5884721663604794\n",
      "7428 0.644257831738978 1135.2637706910723 pop,GOP 0.40249089188645903\n",
      "11345 0.0 0.0005506244755753922 pop,GOP 0.036014682268739315\n",
      "11421 0.012506492236366587 0.00040098215869423075 pop,GOP 0.02128483911559313\n",
      "11439 0.5563113251061264 0.0023806026493683663 pop,GOP 0.1271685381669787\n",
      "11668 2.4016333540799467e-11 6.828597047800082e-07 pop,GOP 3.4763766788800416e-05\n",
      "13054 0.6934927924468234 297.26983789524036 pop,GOP 0.5884734268309642\n",
      "13055 1.7506235498787852e-12 297.26991197631685 pop,GOP 0.5884734614204103\n",
      "13123 0.6573622843736989 0.09400033325303839 pop,GOP 0.6493288590604026\n",
      "13125 0.6725481092242427 0.00031402821814762447 pop,GOP 0.020390758460256824\n",
      "13126 0.6714729342784818 6.890991326726341e-06 pop,GOP 0.0004474519535978345\n",
      "13127 0.697490406822887 853.5166052050216 pop,GOP 0.6493288590604027\n",
      "13200 0.6789795374218268 1108.4308279440477 pop,GOP 0.6893174165901438\n",
      "13205 0.6915413196370624 0.011084952815297415 pop,GOP 0.6893174165901439\n",
      "13209 0.6970567928155281 0.6100471115296119 pop,GOP 0.6893174165901439\n",
      "13227 0.6488055891844909 570.1549226085885 pop,GOP 0.6688442211055277\n",
      "13228 0.6496442974108108 0.004407999591868999 pop,GOP 0.2948265053657105\n",
      "13229 0.685539943626104 395.589450004729 pop,GOP 0.6688442211055277\n",
      "13230 0.643648275850514 238.12771312397751 pop,GOP 0.6688442211055277\n",
      "13231 0.6937838115339297 0.029304079401212473 pop,GOP 0.6688442211055277\n",
      "13232 0.6909900314394171 0.005839542416206225 pop,GOP 0.39057441989801434\n",
      "13233 0.6537951604073903 0.00784270588259779 pop,GOP 0.5245548507405858\n",
      "13235 0.649329639300698 786.0775194723254 pop,GOP 0.6688442211055277\n",
      "13332 0.6964669623396615 0.0018288679700260641 pop,GOP 0.11408132068454499\n",
      "13340 0.6931203197350669 0.0007665039707590237 pop,GOP 0.04781306618481107\n",
      "13347 0.0 297.2800969247053 pop,GOP 0.5884755566578794\n",
      "13349 0.0 297.27412172171074 pop,GOP 0.5884743043420946\n",
      "13350 0.0 297.2788095349732 pop,GOP 0.58847528684394\n",
      "13353 0.0 297.2714884368158 pop,GOP 0.588473752427835\n",
      "13354 0.0 297.29877720652263 pop,GOP 0.588479471449015\n",
      "13363 5.387498790500613e-10 297.2728187028847 pop,GOP 0.5884739858322687\n",
      "13364 2.6414352168520932e-11 297.2712401245964 pop,GOP 0.5884736784001889\n",
      "13374 0.6881771541293558 297.2698818834194 pop,GOP 0.5884734049799343\n",
      "13375 3.320749606389445e-11 297.2851999173473 pop,GOP 0.588477129184942\n",
      "13376 7.163448859391075e-09 297.2697588752437 pop,GOP 0.5884733899228527\n",
      "13377 1.224734987637368e-13 297.27929758358243 pop,GOP 0.5884756998989797\n",
      "13378 0.6510125633921102 297.27040288317403 pop,GOP 0.5884735458860506\n",
      "13379 0.6304464067091458 297.2733849953138 pop,GOP 0.5884742680729225\n",
      "13380 0.6529634413989637 1139.5665301698841 pop,GOP 0.6416852007863476\n",
      "13381 0.6133114166642437 744.9424201578004 pop,GOP 0.6317367708985075\n",
      "13382 0.6840189418335959 297.27107597113024 pop,GOP 0.5884737088910076\n",
      "13411 0.677235493641201 297.30997717581715 pop,GOP 0.588464678941831\n",
      "13412 0.6777592135698599 297.3860676948868 pop,GOP 0.5884482048027148\n",
      "13413 0.6315571251410578 297.2812380656386 pop,GOP 0.5884709033579254\n",
      "13414 0.630987551433833 1205.9429473518696 pop,GOP 0.5399520812883226\n",
      "13415 0.6475927124057557 894.1925119630919 pop,GOP 0.5454862393911655\n",
      "13416 0.6843658026254433 851.6388265660428 pop,GOP 0.5465559171874055\n",
      "13417 0.6441340415127308 982.1532532074693 pop,GOP 0.5435689799879198\n",
      "13451 0.0 297.27767153162 pop,GOP 0.5884757981250698\n",
      "13454 0.0 297.27640947976204 pop,GOP 0.5884754140310886\n",
      "13458 0.6636399564685369 297.2794501590872 pop,GOP 0.5884763394285931\n",
      "13459 0.6049866733848208 297.27415254222626 pop,GOP 0.5884747271445875\n",
      "13483 3.2913317793103997e-15 1604.11935075983 pop,GOP 0.6501104553523964\n",
      "13484 0.0 699.3738670600558 pop,GOP 0.6319726765421628\n",
      "13485 0.0 297.2874536352415 pop,GOP 0.5884778931223462\n",
      "13487 0.0 297.27776154159267 pop,GOP 0.5884754266153357\n",
      "13491 0.6954084002322355 297.2842674553442 pop,GOP 0.5884752264183114\n",
      "13497 1.7745185503436895e-15 1371.2937021598982 pop,GOP 0.6179462232154849\n",
      "13508 1.6471078834551025e-10 297.26976984479967 pop,GOP 0.5884733907811681\n",
      "13509 0.6769379127729389 297.26979712476117 pop,GOP 0.5884733929384328\n",
      "13510 8.677871779961129e-11 297.2699782935296 pop,GOP 0.5884734072650188\n",
      "13511 0.679165006735047 1166.241712225522 pop,GOP 0.6059890970210539\n",
      "13512 0.6473293120861874 899.9172384123714 pop,GOP 0.6042158013561922\n",
      "13514 0.6302724990839559 302.81055694061115 pop,GOP 0.5889035318601296\n",
      "13515 0.6326041535913297 297.27055559558517 pop,GOP 0.5884734529171833\n",
      "13516 0.6758233275816584 297.2700000243308 pop,GOP 0.5884734089834606\n",
      "13518 0.6503898535042761 297.2820295618062 pop,GOP 0.5884743602241199\n",
      "13519 0.6120819909114912 297.27143719607454 pop,GOP 0.5884735226324609\n",
      "13520 0.641316622015146 297.3525317468832 pop,GOP 0.5884799336662447\n",
      "13521 0.6998630847481006 297.53420623696496 pop,GOP 0.5884942834918698\n",
      "13525 0.673371219771549 297.27050256040474 pop,GOP 0.5884734487232497\n",
      "13540 0.6595093212188091 297.39815178244476 pop,GOP 0.5884996499001452\n",
      "13542 0.6256808725419684 297.2928558067132 pop,GOP 0.5884781155693704\n",
      "13549 0.0 297.27032804035105 pop,GOP 0.5884733273923256\n",
      "13555 0.0 297.2697588252939 pop,GOP 0.5884733899086054\n",
      "13571 1.174219980563533e-09 297.2752401540165 pop,GOP 0.588473131870717\n",
      "13578 0.6850601379723031 297.27692601937514 pop,GOP 0.5884730525090665\n",
      "13580 0.6627074834034365 297.2852310766275 pop,GOP 0.5884726615639067\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting underused PRECINCTS\n",
    "for p in range (nPrecincts) :\n",
    "    if precinctUse[p] < 0.7 and isSkippedPrecinct[p] == 0 and vtdPop[p] > 0: #ignore suppressed precincts\n",
    "        print(p,precinctUse[p], vtdPop[p],\"pop,GOP\",vtdGOP[p])\n",
    "        pU = round(precinctUse[p],3)\n",
    "        tractX = vtdGeom[p].centroid.x\n",
    "        tractY = vtdGeom[p].centroid.y        \n",
    "        # print(t, ratio,\"(\",tractX,tractY,\")\",HDvGOP[t], \"t, pop/target,(x,y), pctR\")\n",
    "        plt.text(vtdGeom[p].centroid.x, vtdGeom[p].centroid.y,pU, fontsize=9)\n",
    "        if notPolyVTD[p] == 0 :\n",
    "            x,y = vtdGeom[p].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "        else :\n",
    "            for geom in vtdGeom[p].geoms:\n",
    "                x,y = geom.exterior.xy\n",
    "                plt.plot(x,y)\n",
    "\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"purple\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "id": "37f47a77-d58e-4b4c-be60-676819128e21",
   "metadata": {},
   "outputs": [],
   "source": [
    "# (OPTIONAL) IF WE STOPPED AND RESTARTED, read the data back in:  #NEED TO UPDATE THIS W/ANGLES, RADII\n",
    "import pandas as pd\n",
    "infilename = \"FL_nD28tol0.01nW4.csv\"\n",
    "df2 = pd.read_csv(\"state_HD_output/\"+infilename)\n",
    "HDvPop = df2[\"HD-pop\"]\n",
    "HDvHisp = df2[\"HDvHisp\"]\n",
    "HDvGOP = df2['HDvGOP']\n",
    "HDweight = df2['HDwt']\n",
    "HDarea = df2['HDarea']\n",
    "tractLoopCounter = df2['Loops']\n",
    "tractPop = df2['tractPop']\n",
    "tractCPx = df2['centroid x']\n",
    "tractCPy = df2['centroid y']\n",
    "tractUse = df2['tractUse']\n",
    "# nearEdge = df2['nearEdge']   #add this back in, PLEASE\n",
    "df2.head()\n",
    "nTracts = len(tractPop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e9b2c0c0-fb53-44e5-9155-1963d4833ee8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "750596.7418101031 0.1131774380110593 0.0001361943772 0.0400616118781349 0.7450137754374908\n"
     ]
    }
   ],
   "source": [
    "#  HERE'S A BLOCK TO OVERWRITE THE REDONE DATA\n",
    "for r in range(nRedos):\n",
    "    t = redo[r]\n",
    "    HDvPop[t] = redoHDvPop[r]\n",
    "    HDvGOP[t] = redoHDvGOP[r]\n",
    "    tractArea[t] = redoHDarea[r]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c80e0880-6811-4408-b847-6d70bd2546ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt   #SKIP THIS BLOCK IF STARTING FROM FIRST BLOCK\n",
    "from scipy.stats import norm\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "c8f73bb9-e36b-41c7-9220-1ec09169aa3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# all done with redo's.  Let's look at some outputs.  First, red/blue by home district  SHOULD BE MIX OF RED AND BLUE\n",
    "nPlot = 5\n",
    "for t in range(nTracts):\n",
    "    if(t % nPlot == 0 and tractPop[t] > minTractPop):\n",
    "        redd = min(max( 0, ( HDvGOP[t] - 0.5) * 3.0 ),1)\n",
    "        bluu = min(max( 0, (0.5 - HDvGOP[t]) * 3.0 ),1)\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker='.',color=(redd, 0,bluu ) )\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,color='green')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "a5d3b510-6d1e-49ae-9a5d-03352e2b449e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "map of 8 tracts that were used less than 0.7 of expectation in NJ\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now, where are those UNDERPERFORMING tracts (<0.x usage)\n",
    "maxPlot = 0.70\n",
    "counter = 0\n",
    "for t in range(nTracts):\n",
    "    if(tractUse[t] < maxPlot and tractUse[t] > 0.05):  #ignore skipped tracts\n",
    "        counter +=1\n",
    "        redd = min(max( 0, ( HDvGOP[t] - 0.5) * 3.0 ),1)\n",
    "        bluu = min(max( 0, (0.5 - HDvGOP[t]) * 3.0 ),1)\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker='.',color=(redd, 0,bluu ) )\n",
    "\n",
    "x,y = wholeMAP.exterior.xy   #turn these on if I pull map back in\n",
    "plt.plot(x,y,c=\"green\")\n",
    "print(\"map of {2} tracts that were used less than {0} of expectation in {1}\".format(maxPlot, STATE, counter) )\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "876011e8-d37e-44a6-8dd9-a1e4d2f51e58",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is a map of tracts that were used more than 1.3 of expectation\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ... And where are the OVERUSED tracts?\n",
    "minPlot = 1.3\n",
    "print(\"here is a map of tracts that were used more than {0} of expectation\".format(minPlot) )\n",
    "for t in range(nTracts):\n",
    "    if(tractUse[t] >minPlot and tractUse[t] > 0.05):  #ignore skipped tracts\n",
    "        redd = min(max( 0, ( HDvGOP[t] - 0.5) * 3.0 ),1)\n",
    "        bluu = min(max( 0, (0.5 - HDvGOP[t]) * 3.0 ),1)\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker='.',color=(redd, 0,bluu ) )\n",
    "\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "19bfa7c8-0449-4182-8ed9-f5a1c1276f03",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is a map of 1 tracts that were used more than 1.6 of expectation\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ... And where are the REALLY OVERUSED tracts?\n",
    "minPlot = 1.6\n",
    "counter = 0\n",
    "for t in range(nTracts):\n",
    "    if(tractUse[t] >minPlot and tractUse[t] > 0.05):  #ignore skipped tracts\n",
    "        redd = min(max( 0, ( HDvGOP[t] - 0.5) * 3.0 ),1)\n",
    "        bluu = min(max( 0, (0.5 - HDvGOP[t]) * 3.0 ),1)\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker='.',color=(redd, 0,bluu ) )\n",
    "        counter +=1\n",
    "\n",
    "print(\"here is a map of {0} tracts that were used more than {1} of expectation\".format(counter, minPlot) )\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "f3e9bde3-06f6-402d-bce1-62b5631e8db0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# CORRELATION OF TRACT UNDER-USAGE IN OTHER TRACT'S HOME DISTRICTS vs. being near a boundary\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(tractUse,nearEdge,marker='.' )\n",
    "ax.set(xlabel=\"tract use vs. expectation\", ylabel=\"number of unfilled wedges - max = \"+str(nWedges))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "6bb5c12a-a6a4-4cb1-9026-d227bc11c2ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.10133001263525251 = NJ std dev of TRACT usage.  Here is its weighted histogram\n"
     ]
    },
    {
     "data": {
      "image/png": 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c2N++BzgIXAkcB34KXLd0Jb/CSE8ZrhD70Ab70Ab70IYV70N6N95JktQWnyQhSWqSASVJalKzAbWYxysN23e5dOjDtf3aH03yYJJLB7Y9meS7SR5OMrW8lf9CjcP6sC3Jj/p1Ppzklq77LpcOffjsQP1Hk7yU5Ff721o5DvuSnEpydJ7tq2E8DOvDahgPw/qwGsbDsD60Mx6qqrkfejdj/BfwG8D5wCPAxbPaXAncT+8zWO8D/q3rvg314f3A6/u/7zjTh/7yk8CGVXActgHfOJd9W+nDrPZ/BPxjS8ehX8cHgHcDR+fZ3vR46NiHpsdDxz40PR669GFW2xUdD63OoF5+vFJVvQicebzSoJcfr1RVh4ELkry5477LYWgdVfVgVT3fXzxM7/NjLVnMf8tVcxxmuQa4e1kqW4CqegB47ixNWh8PQ/uwCsZDl+Mwn1VzHGZZ0fHQakDN9+ikLm267LscFlrH9fT+Aj6jgG8leSi9R0SthK59+O0kjyS5P8k7FrjvUutcR5JXAduBrw+sbuE4dNH6eFioFsdDVy2Ph85aGA+tfmHhYh6v1OmxS8ugcx1JrqA3IH93YPXvVNXJJG8E/iHJf/b/8llOXfrwH8Bbq+qFJFcC99J7qv2qOw70Tmd8p6oG/7ps4Th00fp46Kzh8dBF6+NhIVZ8PLQ6g1rM45VaeexSpzqSXALcCeysqmfPrK+qk/1/TwF/S+8UwXIb2oeq+nFVvdD//SBwXpINXfZdJgup42pmnc5o5Dh00fp46KTx8TDUKhgPC7Hy42G5LnYt5IfezO4EsIWfX1B8x6w2V/GLF4X/veu+DfXhLfSevvH+WetfDbx24PcHge2N9uHX+PkHvi8Dnuofk1VzHPrtXkfvvPyrWzsOA/VsZv6L802Ph459aHo8dOxD0+OhSx/625sYD02e4qtFPF5pvn0b7cMtwBuAO5IAnK7e04PfBPxtf9164GtV9c1G+/BR4FNJTgP/C1xdvf+DV9NxAPgI8K2q+snA7k0cB4Akd9O7Q2xDkmngVuA8WB3jATr1oenxAJ360PR4gE59gEbGg486kiQ1qdVrUJKkMWdASZKaZEBJkppkQEmSmmRASZKaZEBJkppkQEmSmvT/7NFngiKQCdoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR tract usage in a histogram\n",
    "n_bins=50\n",
    "avgTractUse = 0.\n",
    "statePop = np.sum(tractPop)\n",
    "for t in range(nTracts):\n",
    "    avgTractUse += tractUse[t] * tractPop[t]/statePop\n",
    "sumVarTractUse = 0.\n",
    "for t in range(nTracts):\n",
    "    sumVarTractUse += (tractUse[t]-avgTractUse)**2 *tractPop[t]/statePop\n",
    "sdTractUse = sumVarTractUse ** 0.5\n",
    "\n",
    "print(sdTractUse,\"=\",STATE,\"std dev of TRACT usage.  Here is its weighted histogram\")\n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(tractUse, bins=n_bins, weights=tractPop)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "121f9869-3d25-4594-93b3-64a57a95aca8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.1150477416806486 = std dev of PRECINCT usage.  Here is its weighted histogram\n",
      "this is a histogram of PRECINCT usage by precinct for NJ\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR precinct usage in a histogram  \n",
    "n_bins=50\n",
    "avgPrecinctUse = 0.\n",
    "stateVTDPop = np.sum(vtdPop)\n",
    "for p in range(nPrecincts):\n",
    "    avgPrecinctUse += precinctUse[p] * vtdPop[p]/stateVTDPop\n",
    "sumVarPrecinctUse = 0.\n",
    "for p in range(nPrecincts):\n",
    "    sumVarPrecinctUse += (precinctUse[p]-avgPrecinctUse)**2 *vtdPop[p]/stateVTDPop\n",
    "sdPrecinctUse = sumVarPrecinctUse ** 0.5\n",
    "\n",
    "print(sdPrecinctUse,\"= std dev of PRECINCT usage.  Here is its weighted histogram\")\n",
    "print(\"this is a histogram of PRECINCT usage by precinct for\",STATE)        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(precinctUse, bins=n_bins, weights=vtdPop)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "f6f03dcb-c722-4789-b47f-fb7aa3be97bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here's a look at numerical stability - number of loops, avg =  6.5447\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "avgTLC = 0.\n",
    "usedTracts = 0.\n",
    "for t in range (nTracts):\n",
    "    if(tractLoopCounter[t] > 0):\n",
    "        avgTLC += tractLoopCounter[t]\n",
    "        usedTracts +=1.\n",
    "avgTLC = round(avgTLC / usedTracts, 4)\n",
    "print(\"here's a look at numerical stability - number of loops, avg = \",avgTLC)\n",
    "n_bins=20     \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(tractLoopCounter, bins=n_bins)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "974631b2-10ef-4847-af2e-a1e26b6fa768",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of home-district pct GOP by tract\n",
      "statewide vote is off by -0.0131 0.40619 HD avg vs true 0.41929\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR HOME DISTRICT red-blue lean in a histogram  and check the statewide vote vs. HD average\n",
    "n_bins=50\n",
    "print(\"this is a histogram of home-district pct GOP by tract\") \n",
    "print(\"statewide vote is off by\",round((stateGOP2-stateGOP),5),round(stateGOP2,5),\"HD avg vs true\",round(stateGOP,5))\n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(HDvGOP, bins=n_bins,weights=HDweight)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "720d9eb5-11e8-45f7-b402-a0048e9ddb99",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calculated statewide Hispanic pct was 0.19057, should have been 0.19722\n",
      "this is a histogram of home-district VAP pct Hispanic by tract\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR home district pct Hispanic in a histogram\n",
    "print(\"calculated statewide Hispanic pct was {0}, should have been {1}\"\n",
    "      .format(round(stateHisp2,5), round(np.sum(tractHisp)/np.sum(tractVAP),5) ) )\n",
    "n_bins=50\n",
    "print(\"this is a histogram of home-district VAP pct Hispanic by tract\")        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(HDvHisp, bins=[0.1,0.15,0.2,0.25,0.3,0.35,0.4,0.45,0.5,0.55,0.6,0.65,0.7,0.75,0.8,0.85,0.9,0.95,1.0],\n",
    "        weights=HDweight)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "1ab7704f-a226-45a9-98a6-d0684522c1eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a bar plot of seats by VAP pct Hispanic for NJ\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ALTERNATE VIEW OF HISPANIC VOTE: HOW MANY DISTRICTS PER 10pct BIN of PCT HISPANIC VOTE?\n",
    "n_bins = 20\n",
    "HispSeats = [0.]*n_bins\n",
    "binMid = [0.]*n_bins\n",
    "for b in range(n_bins):\n",
    "    binMid[b]= float(b)/n_bins + 0.5/n_bins  #centering each bin\n",
    "for t in range(nTracts) :\n",
    "    b = int(HDvHisp[t]*n_bins)\n",
    "    HispSeats[b] += HDweight[t]*nDistricts  #multiply by nDistricts to get number of expected seats\n",
    "\n",
    "print(\"this is a bar plot of seats by VAP pct Hispanic for\",STATE)        \n",
    "fig, ax = plt.subplots()\n",
    "plt.bar(binMid,HispSeats,width=0.09 )\n",
    "#plt.scatter(binMid,HispSeats )\n",
    "ax.set(xlabel=\"pct Hispanic\", ylabel=\"number of seats\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "3ef49161-368a-40a2-83ff-92187edb3462",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using a threshold of 0.35 the number of Hisp districts should be 1.319\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Where in the state do we see Hispanic-heavy home districts?\n",
    "minHisp1 = 0.35\n",
    "minHisp2 = 0.40\n",
    "sumHispDistricts = 0.\n",
    "for t in range(nTracts):\n",
    "    if (HDvHisp[t] > minHisp1 and tractPop[t] > minTractPop):\n",
    "        sumHispDistricts += tractPop[t]/avgDistrictPop\n",
    "        if (HDvHisp[t] < minHisp2):\n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='orange' )\n",
    "        else :            \n",
    "            plt.text(tractCPx[t],tractCPy[t],'o',color='blue', fontsize= 12)\n",
    "print(\"using a threshold of\",minHisp1,\"the number of Hisp districts should be\",round(sumHispDistricts,3))\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "a92133ad-dc95-4aa1-af02-c1d71296cee6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a bar plot of seats by VAP pct Black for NJ\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ALTERNATE VIEW OF BLACK VOTE: HOW MANY DISTRICTS PER 10pct BIN of PCT BLACK VOTE?\n",
    "n_bins = 10\n",
    "BlackSeats = [0.]*n_bins\n",
    "binMid = [0.]*n_bins\n",
    "for b in range(n_bins):\n",
    "    binMid[b]= float(b)/n_bins + 0.5/n_bins  #centering each bin\n",
    "for t in range(nTracts) :\n",
    "    b = int(HDvBlack[t]*n_bins)\n",
    "    BlackSeats[b] += HDweight[t]*nDistricts  #multiply by nDistricts to get number of expected seats\n",
    "\n",
    "print(\"this is a bar plot of seats by VAP pct Black for\",STATE)        \n",
    "fig, ax = plt.subplots()\n",
    "plt.bar(binMid,BlackSeats,width=0.09 )\n",
    "ax.set(xlabel=\"pct Black\", ylabel=\"number of seats\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "b1e9c437-ddf7-4202-aff0-19f338a5d013",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Black candidates would expect to win 4.701 GA seats out of 14\n",
      "Using simple Alabama-style of Blacks voting 0.9 Dem and whites voting 0.8 GOP\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Where in the state do we see opportunity home districts?  #NEW FORMULATION based on ALABAMA\n",
    "isOppor = [0]*nTracts #will track whether this is an opportunity district\n",
    "blackFracSeats = 0.\n",
    "blackPctDem = 0.9  #fraction of Black voters who are Democrats (est'd from Chen / Stephanopoulos)\n",
    "whitePctGOP = 0.8   #fraction of White voters who are Republican  (\"  \")\n",
    "#minBlack1 = 0.30\n",
    "#minBlack2 = 0.50\n",
    "for t in range(nTracts):\n",
    "    pctWhiteDem = (1-whitePctGOP)*(1-HDvBlack[t])  #this is the fraction of voters who are white Democrats\n",
    "    pctBlackDem = blackPctDem * HDvBlack[t]  #fraction of voters who are black Democrats\n",
    "    if tractPop[t] > minTractPop and HDvGOP[t] < 0.5 :  #Dems would win this Home District\n",
    "        if pctBlackDem > pctWhiteDem : #Blacks would win the primary\n",
    "            isOppor[t] = 1\n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='blue' )\n",
    "            blackFracSeats += HDweight[t]\n",
    "        else :  #the White Dem candidate would win the election          \n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='gray' )\n",
    "\n",
    "print(\"Black candidates would expect to win\",round(blackFracSeats*nDistricts,3),STATE,\"seats out of\",nDistricts)\n",
    "print(\"Using simple Alabama-style of Blacks voting\",blackPctDem,\"Dem and whites voting\",whitePctGOP,\"GOP\")\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "c5125128-e4bb-4337-bc5e-3f48d7cf404f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here's the NJ map with Hisp+Black greater than  0.4 or even 0.5\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Where in the state do we see Black+Hispanic heavy home districts?\n",
    "minMinor = 0.40 \n",
    "minMinor2 = 0.50\n",
    "print(\"Here's the\",STATE,\"map with Hisp+Black greater than \",minMinor,\"or even\",minMinor2)\n",
    "for t in range(nTracts):\n",
    "    if ((HDvBlack[t] + HDvHisp[t]) > minMinor and tractPop[t] > minTractPop):\n",
    "        if (HDvBlack[t] + HDvHisp[t]) > minMinor2 :\n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='blue' )\n",
    "        else :            \n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='gray' )\n",
    "\n",
    "x,y = tractMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "58119ef8-13ef-4135-9843-6518025dda07",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of home-district population by tract; avg =  774081.8333333334\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR HOME DISTRICT population in a histogram\n",
    "n_bins=50\n",
    "print(\"this is a histogram of home-district population by tract; avg = \",np.sum(tractPop)/nDistricts)        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "a = avgDistrictPop\n",
    "ax.hist(HDvPop, bins=[0.9*a,0.92*a,0.95*a,0.99*a,1.0*a,1.01*a,1.05*a,1.1*a])   #n_bins\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "1f9101bd-108a-48cb-bee5-3f4e04c18132",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of home-district area by tract\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# more HOME DISTRICT STATS in a histogram\n",
    "n_bins=50\n",
    "print(\"this is a histogram of home-district area by tract\")        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(HDarea, bins=n_bins)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "d8167a80-2b91-4adc-ab34-9e010838213d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What is the correlation of HD area to red-blue lean?\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(HDarea,HDvGOP,marker='.' )\n",
    "ax.set(xlabel=\"tract area\", ylabel=\"home district pct GOP\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "cf95cc7c-e58a-4174-94f6-b6d4c7ccb0ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What is the correlation of tract usage to red-blue lean?\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(HDvGOP,tractUse, marker='.' )\n",
    "ax.set(xlabel=\"home district pct GOP\", ylabel=\"tractUse\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "617384b8-1e0b-45a7-ae09-babe8dd9d429",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What is the correlation of tract usage to tract population?\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(tractPop,tractUse, marker='.' )\n",
    "ax.set(xlabel=\"Census tract population\", ylabel=\"tractUse\")\n",
    "ax.set_xlim(left=0,right=10000)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "91f163ab-6b94-47ee-bd29-be3e751fe2b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# UPDATED VOTES-SEATS CURVE (from votes2seats.ipynb 1/15/22)\n",
    "# LET'S REWORK OUR VOTES - SEATS CALCULATIONS\n",
    "# FIRST, LET'S DO THE BASIC VOTES-TO-SEATS, no fractional seats\n",
    "# INPUTS ARE HDvGOP[nTracts] and HDweight[nTracts]\n",
    "# HDvGOP is the redness lean of each Census tract's Home District\n",
    "# HDweight is the population of this Census tract relative to the state population\n",
    "# stateGOP is the statewide fraction of GOP vote vs. GOP + Dem vote\n",
    "# KEY EQUATION:\n",
    "# expected Seats at a given vote V = sum(HDweight) for tracts with HDvGOP > 0.5 + (V - stateGOP)\n",
    "# for easy calculation, create ~1000 bins, each bin containing HD's in a dV vote window\n",
    "nBins = 1000\n",
    "dV = 1./nBins\n",
    "binWeight = [0.]*nBins\n",
    "\n",
    "for t in range(nTracts):\n",
    "    binNo = int(HDvGOP[t]*nBins)   #which vote bin does this tract's vote go into for the expected statewide vote?\n",
    "    binWeight[binNo] += HDweight[t]\n",
    "    \n",
    "binVote = [0.]*nBins  #this will store the statewide vote for this bin\n",
    "for b in range(nBins) :\n",
    "    binVote[b] = b*dV\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(binVote, binWeight, marker='.',linestyle=\"none\")\n",
    "ax.set(xlabel=\"district pct GOP\", ylabel=\"bin weight\")\n",
    "plt.show()   #this should resemble above histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "85608d3f-c3bd-4d91-9378-16270ceeb9b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#continuing with votes-to-seats, let's compute the S(V) curve with no fractional-seat smearing\n",
    "cumVote = [0.]*nBins\n",
    "for b in range(nBins) :\n",
    "    cumVote[nBins-b-1] = np.sum(binWeight[:(b+1)])  #cumulative weight of all Home Districts below vote b*dV\n",
    "    # print(\"bin, bin vote, cumVote for this bin\",b,b*dV,cumVote[b] )\n",
    "    \n",
    "cumSeats = [0.]*nBins  #this will sum the seats earned for a given statewide vote\n",
    "stateVoteBin = int( (stateGOP+0.5*dV)*nBins )   #which bin (out of 1/dV) holds the statewide total vote?\n",
    "for b in range(nBins) :\n",
    "    bb = int( max(0,min(nBins-1,nBins/2 + b-stateVoteBin)) )\n",
    "    cumSeats[b] = 1. - cumVote[bb]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(binVote, cumSeats, marker='.',linestyle=\"none\")\n",
    "ax.set(xlabel=\"statewide vote, nBins =\"+str(nBins)+\",\"+str(STATE), ylabel=\"GOP seats won (no fractl smearing)\")\n",
    "RANGE = [0.1, 0.9]\n",
    "fifty50 = [0.5, 0.5]\n",
    "expected = [stateGOP, stateGOP]\n",
    "plt.plot(RANGE,fifty50)\n",
    "plt.plot(fifty50,RANGE)\n",
    "plt.plot(expected,RANGE, linestyle=\"--\",color='red')\n",
    "\n",
    "#LET'S ALSO crudely ESTIMATE RESPONSIVENESS near the STATEWIDE VOTE, with a pseudonormal weighting and lst-sq fit\n",
    "stateSigma = 0.03  #User-adjustable, this is the uncertainty in the statewide vote from election to election\n",
    "usedBins = int(stateSigma/dV)\n",
    "nFitPoints = 6*usedBins\n",
    "voteData = [0.]*nFitPoints\n",
    "seatData = [0.]*nFitPoints\n",
    "counter = 0\n",
    "for b in range(nBins):\n",
    "    if ( abs(b-stateVoteBin) <= 2*usedBins ):  #include this S-V pair in our line fit\n",
    "        voteData[counter]=binVote[b]\n",
    "        seatData[counter]=cumSeats[b]\n",
    "        counter += 1\n",
    "        # print(b,counter)\n",
    "        if ( abs(b-stateVoteBin) < usedBins) : #double count this in data set\n",
    "            voteData[counter]=binVote[b]\n",
    "            seatData[counter]=cumSeats[b]\n",
    "            counter +=1\n",
    "fit = np.polyfit(voteData,seatData,1)  #first-order linear regression with old polyfit\n",
    "Rsimple = fit[0]     #slope is fit[0] in y = mx + b, intercept is fit[1]\n",
    "y0 = fit[1]\n",
    "Rx = [stateGOP - 4.*stateSigma, stateGOP + 4.*stateSigma]\n",
    "Ry = [y0 + Rx[0]*Rsimple, y0 + Rx[1]*Rsimple]\n",
    "plt.plot(Rx,Ry ) \n",
    "ax.text(Rx[1]+0.02, Ry[1]+0.02, \"R = \"+str(round(Rsimple,4)), transform=ax.transAxes, fontsize=14,color='purple')\n",
    "expectedSeats = cumSeats[stateVoteBin]\n",
    "ax.text(0.02,expectedSeats+0.03,\"expected seats = \"+str(round(expectedSeats,3)),transform=ax.transAxes,fontsize=9)\n",
    "ax.text(stateGOP+0.01,0.1,\"HD statewide vote = \"+str(round(stateGOP2,3)),transform=ax.transAxes,fontsize=9)\n",
    "Ex = [0,stateGOP ]\n",
    "Ey = [expectedSeats, expectedSeats]\n",
    "plt.plot(Ex,Ey, linestyle='-.',color='red')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "ce47266f-e2ea-4649-b10c-e8225994c122",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#3/6/22 - alternate votes-to-seats with fractional seat smearing.\n",
    "# First, compute the general cdf sliver for -3 sigma to +3 sigma\n",
    "# Then, apply this smearing to the binVote weights\n",
    "seatVar = 0.04\n",
    "nBins = 1000\n",
    "nSigma = 6.  #how many total sigma of deviation we are explicitly modeling; the rest go into the tails\n",
    "nSlivers = 2*int(3*nBins*seatVar)  #budget for 3 sigma on either side of the expected vote\n",
    "sliverWt = [0.]*nSlivers  #each sliver holds the cdf reflecting the relative distance from the mean vote\n",
    "sliverSigma = [0.]*nSlivers\n",
    "totalSliverWt = 0.\n",
    "for nS in range(nSlivers):\n",
    "    dSigmaHigh = nSigma* (nS+0.5 - nSlivers/2) / float(nSlivers)\n",
    "    dSigmaLow =  nSigma* (nS-0.5 - nSlivers/2) / float(nSlivers)\n",
    "    sliverSigma[nS] = 0.5*(dSigmaHigh+dSigmaLow)  #for plotting; keeps track of this sliver's center sigma\n",
    "    sliverWt[nS] = norm.cdf(dSigmaHigh) - norm.cdf(dSigmaLow)\n",
    "    totalSliverWt += sliverWt[nS]\n",
    "lowSliverWt = 0.5 * (1. - totalSliverWt)\n",
    "highSliverWt = lowSliverWt\n",
    "#print(\"each tail of distribution has weight\",round(lowSliverWt,4) )\n",
    "#plt.plot(sliverSigma,sliverWt)\n",
    "#plt.show()\n",
    "# OK, that worked as expected.  Now, do the smearing of each bin\n",
    "smearedBinWeight = [0.]*nBins\n",
    "for b in range(nBins):\n",
    "    centerBinNo = b #int(HDvGOP[t]*nBins)   #which vote bin does this tract's vote go into for the expected statewide vote?\n",
    "    binOffset = int(nSlivers/2)\n",
    "    for nS in range(nSlivers):\n",
    "        binNo = centerBinNo + nS - binOffset\n",
    "        if (binNo < 0): #deep blue district; variance would push vote %R < 0\n",
    "            binNo = 0\n",
    "        if (binNo >= nBins):  #deep red district; variance would push vote %R > 1\n",
    "            binNo = nBins - 1\n",
    "        smearedBinWeight[binNo] += binWeight[b]*sliverWt[nS]\n",
    "    # Now put the tails into the correct bins\n",
    "    binNo = centerBinNo -1 - binOffset  #left tail\n",
    "    if binNo < 0:\n",
    "        binNo = 0\n",
    "    smearedBinWeight[binNo] += binWeight[b]*lowSliverWt\n",
    "    binNo = centerBinNo + nSlivers - binOffset  #right tail\n",
    "    if binNo >= nBins:\n",
    "        binNo = nBins -1 \n",
    "    smearedBinWeight[binNo] += binWeight[b]*highSliverWt\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(binVote, binWeight, marker='.',linestyle=\"none\",label=\"unsmeared\")\n",
    "plt.plot(binVote, smearedBinWeight, marker='o',linestyle=\"none\",label=\"smeared\")\n",
    "RANGE = [0.0, 0.5*np.max(binWeight)]\n",
    "sGOP = [stateGOP, stateGOP]\n",
    "plt.plot(sGOP,RANGE,linestyle=\"-\",label=\"statewide \"+str(round(stateGOP,3)) )\n",
    "ax.set(xlabel=\"district pct GOP\", ylabel=\"bin weight\")\n",
    "plt.legend()\n",
    "plt.show()   #this should resemble above histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "8a2d7de3-ea93-4447-bb69-36defbae38df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#continuing with votes-to-seats, let's compute the S(V) curve with no fractional-seat smearing\n",
    "smearedBinVote = [0.]*nBins  #this will store the statewide vote for this bin\n",
    "cumSmearedVote = [0.]*nBins\n",
    "for b in range(nBins) :\n",
    "    smearedBinVote[b] = b*dV\n",
    "    cumSmearedVote[nBins-b-1] = np.sum(smearedBinWeight[:(b+1)])  #cumulative weight of all Home Districts below vote b*dV\n",
    "    # print(\"bin, bin vote, cumVote for this bin\",b,b*dV,cumVote[b] )\n",
    "    \n",
    "cumSmearedSeats = [0.]*nBins  #this will sum the seats earned for a given statewide vote\n",
    "stateVoteBin = int( (stateGOP+0.5*dV)*nBins )   #which bin (out of 1/dV) holds the statewide total vote?\n",
    "for b in range(nBins) :\n",
    "    bb = int( max(0,min(nBins-1,nBins/2 + b-stateVoteBin)) )\n",
    "    cumSmearedSeats[b] = 1. - cumSmearedVote[bb]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(smearedBinVote,cumSmearedSeats, marker='o',linestyle=\"none\",label=str(seatVar)+\" smear\")\n",
    "plt.plot(binVote, cumSeats, marker='.',linestyle=\"none\",label=\"no smear\")\n",
    "ax.set(xlabel=\"true \"+STATE+\"-wide vote=\"+str(round(stateGOP,4))+\", nBins =\"+str(nBins), ylabel=\"GOP seats won\")\n",
    "RANGE = [0.1, 0.9]\n",
    "fifty50 = [0.5, 0.5]\n",
    "expected = [stateGOP, stateGOP]\n",
    "expectedSeats = cumSeats[stateVoteBin]\n",
    "expectedS = [expectedSeats,expectedSeats]\n",
    "smearedSeats = cumSmearedSeats[stateVoteBin]\n",
    "smearedS = [smearedSeats,smearedSeats]\n",
    "plt.plot(RANGE,fifty50)\n",
    "plt.plot(fifty50,RANGE)\n",
    "plt.plot(RANGE,smearedS, linestyle=\"--\",color='blue',label=str(round(expectedSeats,3))+\"no smear\")\n",
    "plt.plot(RANGE,expectedS, linestyle=\"--\",color='orange',label=str(round(smearedSeats,3))+\"smeared\")\n",
    "plt.plot(expected,RANGE, linestyle=\"--\",color='red')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "597b7758-4f83-40f7-8909-4946e64267ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "simpler and fractional-seat-smeared (var of 0.04 ) responsiveness are 2.465 2.428\n",
      "fractional expected GOP seats =  2.293  out of  12 seats for NJ\n",
      "simpler expected GOP seats =  2.108  out of  12 seats\n"
     ]
    }
   ],
   "source": [
    "#Finally, let's compare responsiveness using smeared (fractional seat variance) to non-smeared calculated above\n",
    "#LET'S ALSO crudely ESTIMATE (smeared) RESPONSIVENESS near the STATEWIDE VOTE, as we did for non-smeared\n",
    "stateSigma = 0.03  #User-adjustable, this is the uncertainty in the statewide vote from election to election\n",
    "usedBins = int(stateSigma/dV)\n",
    "nFitPoints = 6*usedBins\n",
    "voteData = [0.]*nFitPoints\n",
    "seatData = [0.]*nFitPoints\n",
    "counter = 0\n",
    "for b in range(nBins):\n",
    "    if ( abs(b-stateVoteBin) <= 2*usedBins ):  #include this S-V pair in our line fit\n",
    "        voteData[counter]=smearedBinVote[b]\n",
    "        seatData[counter]=cumSmearedSeats[b]\n",
    "        counter += 1\n",
    "        # print(b,counter)\n",
    "        if ( abs(b-stateVoteBin) < usedBins) : #double count this in data set\n",
    "            voteData[counter]=smearedBinVote[b]\n",
    "            seatData[counter]=cumSmearedSeats[b]\n",
    "            counter +=1\n",
    "fit = np.polyfit(voteData,seatData,1)  #first-order linear regression with old polyfit\n",
    "Rsmeared = fit[0]     #slope is fit[0] in y = mx + b, intercept is fit[1]\n",
    "y0 = fit[1]\n",
    "\n",
    "print(\"simpler and fractional-seat-smeared (var of\",seatVar,\") responsiveness are\",round(Rsimple,3) ,round(Rsmeared,3) )   \n",
    "print(\"fractional expected GOP seats = \",round(nDistricts*smearedSeats,3),\" out of \",nDistricts,\"seats for\",STATE)\n",
    "print(\"simpler expected GOP seats = \",round(nDistricts*expectedSeats,4),\" out of \",nDistricts,\"seats\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "86f82727-7193-4194-aa61-2b99408dbcb6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "language": "python",
   "name": "python3"
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